AIICS

Jonas Kvarnström

All Publications

Hide abstracts BibTeX entries
2014
[48] Oleg Burdakov, Patrick Doherty and Jonas Kvarnstr√∂m. 2014.
Local Search for Hop-constrained Directed Steiner Tree Problem with Application to UAV-based Multi-target Surveillance.
In Butenko, S., Pasiliao, E.L., Shylo, V., editors, Examining Robustness and Vulnerability of Networked Systems, pages 26–50. In series: NATO Science for Peace and Security Series - D: Information and Communication Security #Volume 37. IOS Press. ISBN: 978-1-61499-390-2 (print), 978-1-61499-391-9 (online).
DOI: 10.3233/978-1-61499-391-9-26.
Find book in another country/Hitta boken i ett annat land: http://www.worldcat.org/search?q=978-1-6...

We consider the directed Steiner tree problem (DSTP) with a constraint on the total number of arcs (hops) in the tree. This problem is known to be NP-hard, and therefore, only heuristics can be applied in the case of its large-scale instances.For the hop-constrained DSTP, we propose local search strategies aimed at improving any heuristically produced initial Steiner tree. They are based on solving a sequence of hop-constrained shortest path problems for which we have recently developed efficient label correcting algorithms.The presented approach is applied to finding suitable 3D locations where unmanned aerial vehicles (UAVs) can be placed to relay information gathered in multi-target monitoring and surveillance. The efficiency of our algorithms is illustrated by results of numerical experiments involving problem instances with up to 40 000 nodes and up to 20 million arcs.

[47] Oleg Burdakov, Patrick Doherty and Jonas Kvarnstr√∂m. 2014.
Optimal Scheduling for Replacing Perimeter Guarding Unmanned Aerial Vehicles.
Technical Report. In series: LiTH-MAT-R #2014:09. Link√∂ping University Electronic Press. 13 pages.

Guarding the perimeter of an area in order to detect potential intruders is an important task in a variety of security-related applications. This task can in many circumstances be performed by a set of camera-equipped unmanned aerial vehicles (UAVs). Such UAVs will occasionally require refueling or recharging, in which case they must temporarily be replaced by other UAVs in order to maintain complete surveillance of the perimeter. In this paper we consider the problem of scheduling such replacements. We present optimal replacement strategies and justify their optimality.

[46] Oleg Burdakov, Patrick Doherty and Jonas Kvarnstr√∂m. 2014.
Local Search for Hop-constrained Directed Steiner Tree Problem with Application to UAV-based Multi-target Surveillance.
Technical Report. In series: LiTH-MAT-R #2014:10. Link√∂ping University Electronic Press. 25 pages.

We consider the directed Steiner tree problem (DSTP) with a constraint on the total number of arcs (hops) in the tree. This problem is known to be NP-hard, and therefore, only heuristics can be applied in the case of its large-scale instances. For the hop-constrained DSTP, we propose local search strategies aimed at improving any heuristically produced initial Steiner tree. They are based on solving a sequence of hop-constrained shortest path problems for which we have recently developed ecient label correcting algorithms. The presented approach is applied to nding suitable 3D locations where unmanned aerial vehicles (UAVs) can be placed to relay information gathered in multi-target monitoring and surveillance. The eciency of our algorithms is illustrated by results of numerical experiments involving problem instances with up to 40 000 nodes and up to 20 million arcs.

[45] Mikael Nilsson, Jonas Kvarnstr√∂m and Patrick Doherty. 2014.
Incremental Dynamic Controllability in Cubic Worst-Case Time.
In Proceedings of the 21th International Symposium on Temporal Representation and Reasoning (TIME).
Note: Accepted for Publication.

It is generally hard to predict the exact duration of an action. The uncertainty in the duration is often modeled in temporal planning by the use of upper bounds on durations, with the assumption that if an action happens to be executed more quickly, the plan will still succeed. However, this assumption is often false: If we finish cooking too early, the dinner will be cold before everyone is ready to eat. Simple Temporal Problems with Uncertainty (STPUs) allow us to model such situations. An STPU-based planner must verify that the plans it generates are executable, captured by the property of dynamic controllability. The EfficientIDC (EIDC) algorithm can do this incrementally during planning, with an amortized complexity per step of $O(n^3)$ but a worst-case complexity per step of $O(n^4)$. In this paper we show that the worst-case run-time of EIDC does occur, leading to repeated reprocessing of nodes in the STPU while verifying the dynamic controllability property. We present a new version of the algorithm, called EIDC2, which through optimal ordering of nodes avoids any need for reprocessing. This gives EIDC2 a strictly lower worst-case run-time, making it the fastest known algorithm for incrementally verifying dynamic controllability of STPUs.

[44] Full text  Mikael Nilsson, Jonas Kvarnstr√∂m and Patrick Doherty. 2014.
EfficientIDC: A Faster Incremental Dynamic Controllability Algorithm.
In Proceedings of the 24th International Conference on Automated Planning and Scheduling (ICAPS), pages 199–207. ISBN: ISBN:978-1-57735-660-8.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are uncontrollable (determined by nature), as is often the case for actions in planning. It is essential to verify that such networks are dynamically controllable (DC) ‚Äď executable regardless of the outcomes of uncontrollable durations ‚Äď and to convert them to an executable form. We use insights from incremental DC verification algorithms to re-analyze the original verification algorithm. This algorithm, thought to be pseudo-polynomial and subsumed by an O(n5) algorithm and later an O(n4) algorithm, is in fact O(n4) given a small modification. This makes the algorithm attractive once again, given its basis in a less complex and more intuitive theory. Finally, we discuss a change reducing the amount of work performed by the algorithm.

[43] Full text  Mikael Nilsson, Jonas Kvarnstr√∂m and Patrick Doherty. 2014.
Classical Dynamic Controllability Revisited: A Tighter Bound on the Classical Algorithm.
In Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART), pages 130–141.
DOI: 10.5220/0004815801300141.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems wheresome durations are uncontrollable (determined by nature), as is often the case for actions in planning. It is essentialto verify that such networks are dynamically controllable (DC) ‚Äď executable regardless of the outcomesof uncontrollable durations ‚Äď and to convert them to an executable form. We use insights from incrementalDC verification algorithms to re-analyze the original verification algorithm. This algorithm, thought to bepseudo-polynomial and subsumed by an O(n<sup>5</sup>) algorithm and later an O(n<sup>4</sup>) algorithm, is in fact O(n<sup>4</sup>) givena small modification. This makes the algorithm attractive once again, given its basis in a less complex andmore intuitive theory. Finally, we discuss a change reducing the amount of work performed by the algorithm.

2013
[42] Full text  Patrick Doherty, Fredrik Heintz and Jonas Kvarnstr√∂m. 2013.
Robotics, Temporal Logic and Stream Reasoning.
In Proceedings of Logic for Programming Artificial Intelligence and Reasoning (LPAR), 2013.

[41] Full text  Patrick Doherty, Fredrik Heintz and Jonas Kvarnstr√∂m. 2013.
High-level Mission Specification and Planning for Collaborative Unmanned Aircraft Systems using Delegation.
Unmanned Systems, 1(1):75–119. World Scientific.

Automated specification, generation and execution of high level missions involving one or more heterogeneous unmanned aircraft systems is in its infancy. Much previous effort has been focused on the development of air vehicle platforms themselves together with the avionics and sensor subsystems that implement basic navigational skills. In order to increase the degree of autonomy in such systems so they can successfully participate in more complex mission scenarios such as those considered in emergency rescue that also include ongoing interactions with human operators, new architectural components and functionalities will be required to aid not only human operators in mission planning, but also the unmanned aircraft systems themselves in the automatic generation, execution and partial verification of mission plans to achieve mission goals. This article proposes a formal framework and architecture based on the unifying concept of delegation that can be used for the automated specification, generation and execution of high-level collaborative missions involving one or more air vehicles platforms and human operators. We describe an agent-based software architecture, a temporal logic based mission specification language, a distributed temporal planner and a task specification language that when integrated provide a basis for the generation, instantiation and execution of complex collaborative missions on heterogeneous air vehicle systems. A prototype of the framework is operational in a number of autonomous unmanned aircraft systems developed in our research lab.

[40] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2013.
Stream-Based Hierarchical Anchoring.
K√ľnstliche Intelligenz, 27(2):119–128. Springer.
DOI: 10.1007/s13218-013-0239-2.

Autonomous systems situated in the real world often need to recognize, track, and reason about various types of physical objects. In order to allow reasoning at a symbolic level, one must create and continuously maintain a correlation between symbols denoting physical objects and sensor data being collected about them, a process called anchoring.In this paper we present a stream-based hierarchical anchoring framework. A classification hierarchy is associated with expressive conditions for hypothesizing the type and identity of an object given streams of temporally tagged sensor data. The anchoring process constructs and maintains a set of object linkage structures representing the best possible hypotheses at any time. Each hypothesis can be incrementally generalized or narrowed down as new sensor data arrives. Symbols can be associated with an object at any level of classification, permitting symbolic reasoning on different levels of abstraction. The approach is integrated in the DyKnow knowledge processing middleware and has been applied to an unmanned aerial vehicle traffic monitoring application.

[39] Full text  H√•kan Warnquist, Jonas Kvarnstr√∂m and Patrick Doherty. 2013.
Exploiting Fully Observable and Deterministic Structures in Goal POMDPs.
In Daniel Borrajo, Subbarao Kambhampati, Angelo Oddi, Simone Fratini, editors, Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press. ISBN: 978-1-57735-609-7.

When parts of the states in a goal POMDP are fully observable and some actions are deterministic it is possibleto take advantage of these properties to efficiently generate approximate solutions. Actions that deterministically affect the fully observable component of the world state can be abstracted away and combined into macro actions, permitting a planner to converge more quickly. This processing can be separated from the main search procedure, allowing us to leverage existing POMDP solvers. Theoretical results show how a POMDP can be analyzed to identify the exploitable properties and formal guarantees are provided showing that the use of macro actions preserves solvability. The efficiency of the method is demonstrated with examples when used in combination with existing POMDP solvers.

[38] Full text  Mikael Nilsson, Jonas Kvarnstr√∂m and Patrick Doherty. 2013.
Incremental Dynamic Controllability Revisited.
In Proceedings of the 23rd International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press. ISBN: 978-1-57735-609-7.

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are determined by nature, as is often the case for actions in planning. As such networks are generated it is essential to verify that they are dynamically controllable ‚Äď executable regardless of the outcomes of uncontrollable durations ‚Äď and to convert them to a dispatchable form. The previously published FastIDC algorithm achieves this incrementally and can therefore be used efficiently during plan construction. In this paper we show that FastIDC is not sound when new constraints are added, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm, pinpoint the cause, and show how the algorithm can be modified to correctly detect uncontrollable networks.

2012
[37] Full text  Patrick Doherty, Jonas Kvarnstr√∂m and Andrzej Szalas. 2012.
Temporal Composite Actions with Constraints.
In Proceedings of the 13th International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 478–488. AAAI Press. ISBN: 978-1-57735-560-1, 978-1-57735-561-8.
Link: http://www.aaai.org/ocs/index.php/KR/KR1...

Complex mission or task specification languages play a fundamentally important role in human/robotic interaction. In realistic scenarios such as emergency response, specifying temporal, resource and other constraints on a mission is an essential component due to the dynamic and contingent nature of the operational environments. It is also desirable that in addition to having a formal semantics, the language should be sufficiently expressive, pragmatic and abstract. The main goal of this paper is to propose a mission specification language that meets these requirements. It is based on extending both the syntax and semantics of a well-established formalism for reasoning about action and change, Temporal Action Logic (TAL), in order to represent temporal composite actions with constraints. Fixpoints are required to specify loops and recursion in the extended language. The results include a sound and complete proof theory for this extension. To ensure that the composite language constructs are adequately grounded in the pragmatic operation of robotic systems, Task Specification Trees (TSTs) and their mapping to these constructs are proposed. The expressive and pragmatic adequacy of this approach is demonstrated using an emergency response scenario.

2011
[36] Full text  Jonas Kvarnstr√∂m. 2011.
Planning for Loosely Coupled Agents Using Partial Order Forward-Chaining.
In Fahiem Bacchus, Carmel Domshlak, Stefan Edelkamp, Malte Helmert, editors, Proceedings of the 21st International Conference on Automated Planning and Scheduling (ICAPS), pages 138–145. AAAI Press. ISBN: 978-1-57735-503-8, 978-1-57735-504-5.
Fulltext: http://www.aaai.org/ocs/index.php/ICAPS/...

We investigate a hybrid between temporal partial-order and forward-chaining planning where each action in a partially ordered plan is associated with a partially defined state. The focus is on centralized planning for multi-agent domains and on loose commitment to the precedence between actions belonging to distinct agents, leading to execution schedules that are flexible where it matters the most. Each agent, on the other hand, has a sequential thread of execution reminiscent of forward-chaining. This results in strong and informative agent-specific partial states that can be used for partial evaluation of preconditions as well as precondition control formulas used as guidance. Empirical evaluation shows the resulting planner to be competitive with TLplan and TALplanner, two other planners based on control formulas, while using a considerably more expressive and flexible plan structure.

2010
[35] Full text  Patrick Doherty, Jonas Kvarnstr√∂m, Fredrik Heintz, David Land√©n and Per-Magnus Olsson. 2010.
Research with Collaborative Unmanned Aircraft Systems.
In Gerhard Lakemeyer, Hector J. Levesque, Fiora Pirri, editors, Proceedings of the Dagstuhl Workshop on Cognitive Robotics. In series: Dagstuhl Seminar Proceedings #10081. Leibniz-Zentrum f√ľr Informatik.

We provide an overview of ongoing research which targets development of a principled framework for mixed-initiative interaction with unmanned aircraft systems (UAS). UASs are now becoming technologically mature enough to be integrated into civil society. Principled interaction between UASs and human resources is an essential component in their future uses in complex emergency services or bluelight scenarios. In our current research, we have targeted a triad of fundamental, interdependent conceptual issues: delegation, mixed- initiative interaction and adjustable autonomy, that is being used as a basis for developing a principled and well-defined framework for interaction. This can be used to clarify, validate and verify different types of interaction between human operators and UAS systems both theoretically and practically in UAS experimentation with our deployed platforms.

[34] Full text  Mariusz Wzorek, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Choosing Path Replanning Strategies for Unmanned Aircraft Systems.
In Ronen Brafman, H√©ctor Geffner, J√∂rg Hoffmann, Henry Kautz, editors, Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS), pages 193–200. AAAI Press. ISBN: 978-1-57735-449-9.

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no- fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may in- validate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required.Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learn- ing techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.

[33] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Stream-Based Reasoning in DyKnow.
In Gerhard Lakemeyer and Hector J. Levesque and Fiora Pirri, editors, Proceedings of the Dagstuhl Workshop on Cognitive Robotics. In series: Dagstuhl Seminar Proceedings #10081. Leibniz-Zentrum f√ľr Informatik.

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal kpl language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.

[32] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Stream-Based Middleware Support for Embedded Reasoning.
In Proceedings of the AAAI Spring Symposium on Embedded Reasoning: Intelligence in Embedded Systems (ER).

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. In order to make use of diverse reasoning modules in such systems, issues of integration such as sensor data flow and information flow between such modules has to be taken into account. The DyKnow framework is a tool with a formal basis that pragmatically deals with many of the architectural issues which arise in such systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. DyKnow has proven to be quite robust and widely applicable to different aspects of hybrid software architectures for robotics.In this paper, we describe the DyKnow framework and show how it is integrated and used in unmanned aerial vehicle systems developed in our group. In particular, we focus on issues pertaining to the sense-reasoning gap and the symbol grounding problem and the use of DyKnow as a means of generating semantic structures representing situational awareness for such systems. We also discuss the use of DyKnow in the context of automated planning, in particular execution monitoring.

[31] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Stream-Based Reasoning Support for Autonomous Systems.
In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI). In series: Frontiers in Artificial Intelligence and Applications #215. IOS Press. ISBN: 978-1-60750-605-8.
DOI: 10.3233/978-1-60750-606-5-183.

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based knowledge processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment. DyKnow has a formal basis and pragmatically deals with many of the architectural issues which arise in autonomous systems. This includes a systematic stream-based method for handling the sense-reasoning gap, caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many highlevel reasoning modules. As concrete examples, stream-based support for anchoring and planning are presented.

[30] Full text  Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Automated Planning for Collaborative UAV Systems.
In Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), pages 1078–1085. IEEE conference proceedings. ISBN: 978-1-4244-7813-2, 978-1-4244-7814-9.
DOI: 10.1109/ICARCV.2010.5707969.
IEEE Explore: http://ieeexplore.ieee.org/xpls/abs_all....

Mission planning for collaborative Unmanned Aircraft Systems (UAS:s) is a complex topic which involves trade-offs between the degree of centralization or decentralization required, the degree of abstraction in which plans are generated, and the degree to which such plans are distributed among participating UAS:s. In realistic environments such as those found in naturaland man-made catastrophes where emergency services personnelare involved, a certain degree of centralization and abstractionis necessary in order for those in charge to understand andeventually sign off on potential plans. It is also quite often thecase that unconstrained distribution of actions is inconsistentwith the loosely coupled interactions and dependencies whicharise between collaborating systems. In this article, we presenta new planning algorithm for collaborative UAS:s based oncombining ideas from forward chaining planning with partialorderplanning leading to a new hybrid partial order forwardchaining(POFC) framework which meets the requirements oncentralization, abstraction and distribution we find in realisticemergency services settings.

[29] Full text  Per-Magnus Olsson, Jonas Kvarnstr√∂m, Patrick Doherty, Oleg Burdakov and Kaj Holmberg. 2010.
Generating UAV Communication Networks for Monitoring and Surveillance.
In Proceedings of the 11th International Conference on Control, Automation, Robotics and Vision (ICARCV 2010), pages 1070–1077. IEEE conference proceedings. ISBN: 978-1-4244-7814-9.
DOI: 10.1109/ICARCV.2010.5707968.

An important use of unmanned aerial vehicles is surveillance of distant targets, where sensor information must quickly be transmitted back to a base station. In many cases, high uninterrupted bandwidth requires line-of-sight between sender and transmitter to minimize quality degradation. Communication range is typically limited, especially when smaller UAVs are used. Both problems can be solved by creating relay chains for surveillance of a single target, and relay trees for simultaneous surveillance of multiple targets. In this paper, we show how such chains and trees can be calculated. For relay chains we create a set of chains offering different trade-offs between the number of UAVs in the chain and the chain’s cost. We also show new results on how relay trees can be quickly calculated and then incrementally improved if necessary. Encouraging empirical results for improvement of relay trees are presented.

[28] Full text  H√•kan Warnquist, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Iterative Bounding LAO*.
In Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), pages 341–346. In series: Frontiers in Artificial Intelligence and Applications #215. IOS Press. ISBN: 978-1-60750-605-8, 978-1-60750-606-5.
DOI: 10.3233/978-1-60750-606-5-341.

Iterative Bounding LAO* is a new algorithm for epsilon- optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given initial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.

[27] Full text  Jonas Kvarnstr√∂m. 2010.
Planning for Loosely Coupled Agents using Partial Order Forward-Chaining.
In Roland Bol, editor, The Swedish AI Society Workshop 2010, SAIS 2010, pages 45–54. In series: Link√∂ping Electronic Conference Proceedings #48. Link√∂ping University Electronic Press, Link√∂pings universitet.
Fulltext: http://www.ep.liu.se/ecp/048/009/ecp1048...

Partially ordered plan structures are highly suitable for centralized multi-agent planning, where plans should be minimally constrained in terms of precedence between actions performed by different agents. In many cases, however, any given agent will perform its own actions in strict sequence. We take advantage of this fact to develop a hybrid of temporal partial order planning and forward-chaining planning. A sequence of actions is constructed for each agent and linked to other agents' actions by a partially ordered precedence relation as required. When agents are not too tightly coupled, this structure enables the generation of partial but strong information about the state at the end of each agent's action sequence. Such state information can be effectively exploited during search. A prototype planner within this framework has been implemented, using precondition control formulas to guide the search process.

[26] Full text  Oleg Burdakov, Patrick Doherty, Kaj Holmberg, Jonas Kvarnstr√∂m and Per-Magnus Olsson. 2010.
Relay Positioning for Unmanned Aerial Vehicle Surveillance.
The international journal of robotics research, 29(8):1069–1087. Sage Publications.
DOI: 10.1177/0278364910369463.

When unmanned aerial vehicles (UAVs) are used for surveillance, information must often be transmitted to a base station in real time. However, limited communication ranges and the common requirement of free line of sight may make direct transmissions from distant targets impossible. This problem can be solved using relay chains consisting of one or more intermediate relay UAVs. This leads to the problem of positioning such relays given known obstacles, while taking into account a possibly mission-specific quality measure. The maximum quality of a chain may depend strongly on the number of UAVs allocated. Therefore, it is desirable to either generate a chain of maximum quality given the available UAVs or allow a choice from a spectrum of Pareto-optimal chains corresponding to different trade-offs between the number of UAVs used and the resulting quality. In this article, we define several problem variations in a continuous three-dimensional setting. We show how sets of Pareto-optimal chains can be generated using graph search and present a new label-correcting algorithm generating such chains significantly more efficiently than the best-known algorithms in the literature. Finally, we present a new dual ascent algorithm with better performance for certain tasks and situations.

[25] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2010.
Bridging the sense-reasoning gap: DyKnow - Stream-based middleware for knowledge processing.
Advanced Engineering Informatics, 24(1):14–26. Elsevier.
DOI: 10.1016/j.aei.2009.08.007.

Engineering autonomous agents that display rational and goal-directed behavior in dynamic physical environments requires a steady flow of information from sensors to high-level reasoning components. However, while sensors tend to generate noisy and incomplete quantitative data, reasoning often requires crisp symbolic knowledge. The gap between sensing and reasoning is quite wide, and cannot in general be bridged in a single step. Instead, this task requires a more general approach to integrating and organizing multiple forms of information and knowledge processing on different levels of abstraction in a structured and principled manner. We propose knowledge processing middleware as a systematic approach to organizing such processing. Desirable properties are presented and motivated. We argue that a declarative stream-based system is appropriate for the required functionality and present DyKnow, a concrete implemented instantiation of stream-based knowledge processing middleware with a formal semantics. Several types of knowledge processes are defined and motivated in the context of a UAV traffic monitoring application. In the implemented application, DyKnow is used to incrementally bridge the sense-reasoning gap and generate partial logical models of the environment over which metric temporal logical formulas are evaluated. Using such formulas, hypotheses are formed and validated about the type of vehicles being observed. DyKnow is also used to generate event streams representing for example changes in qualitative spatial relations, which are used to detect traffic violations expressed as declarative chronicles.

[24] Oleg Burdakov, Patrick Doherty, Kaj Holmberg, Jonas Kvarnstr√∂m and Per-Magnus Olsson. 2010.
Positioning Unmanned Aerial Vehicles As Communication Relays for Surveillance Tasks.
In J. Trinkle, Y. Matsuoka and J.A. Castellanos, editors, Robotics: Science and Systems V, pages 257–264. MIT Press. ISBN: 978-0-262-51463-7.
find book at a swedish library/hitta boken i ett svenskt bibliotek: http://libris.kb.se/bib/12536749
Link to publication: http://www.roboticsproceedings.org/rss05...

When unmanned aerial vehicles (UAVs) are used to survey distant targets, it is important to transmit sensor information back to a base station. As this communication often requires high uninterrupted bandwidth, the surveying UAV often needs afree line-of-sight to the base station, which can be problematic in urban or mountainous areas. Communication ranges may also belimited, especially for smaller UAVs. Though both problems can be solved through the use of relay chains consisting of one or more intermediate relay UAVs, this leads to a new problem: Where should relays be placed for optimum performance? We present two new algorithms capable of generating such relay chains, one being a dual ascent algorithm and the other a modification of the Bellman-Ford algorithm. As the priorities between the numberof hops in the relay chain and the cost of the chain may vary, wecalculate chains of different lengths and costs and let the ground operator choose between them. Several different formulations for edge costs are presented. In our test cases, both algorithms are substantially faster than an optimized version of the original Bellman-Ford algorithm, which is used for comparison.

2009
[23] Full text  H√•kan Warnquist, Jonas Kvarnstr√∂m and Patrick Doherty. 2009.
Planning as Heuristic Search for Incremental Fault Diagnosis and Repair.
In Proceedings of the Scheduling and Planning Applications Workshop (SPARK) at the 19th International Conference on Automated Planning and Scheduling (ICAPS).

In this paper we study the problem of incremental fault diagnosis and repair of mechatronic systems where the task is to choose actions such that the expected cost of repair is minimal. This is done by interleaving acting with the generation of partial conditional plans used to decide the next action. A diagnostic model based on Bayesian Networks is used to update the current belief state after each action. The planner uses a simplified version of this model to update predicted belief states. We have tested the approach in the domain of troubleshooting heavy vehicles. Experiments show that a simplified model for planning improves performance when troubleshooting with limited time.

[22] Full text  Patrick Doherty and Jonas Kvarnstr√∂m. 2009.
Temporal Action Logics.
In V. Lifschitz, F. van Harmelen, and F. Porter, editors, Handbook of Knowledge Representation, pages 709–757. In series: Foundations of Artificial Intelligence #3. Elsevier. ISBN: 978-0-444-52211-5.
DOI: 10.1016/S1574-6526(07)03018-0.
find book at a swedish library/hitta boken i ett svenskt bibliotek: http://libris.kb.se/hitlist?d=libris&q=9...

The study of frameworks and formalisms for reasoning about action and change [67, 58, 61, 65, 70, 3, 57] has been central to the knowledge representation field almost from the inception of Artificial Intelligence as a general field of research [52, 56]. The phrase ‚ÄúTemporal Action Logics‚ÄĚ represents a class of logics for reasoning about action and change that evolved from Sandewall‚Äôs book on Features and Fluents [61] and owes much to this ambitious project. There are essentially three major parts to Sandewall‚Äôs work. He first developed a narrative-based logical framework for specifying agent behavior in terms of action scenarios. The logical framework is state-based and uses explicit time structures. He then developed a formal framework for assessing the correctness (soundness and completeness) of logics for reasoning about action and change relative to a set of well-defined intended conclusions, where reasoning problems were classified according to their ontological or epistemological characteristics. Finally, he proposed a number of logics defined semantically in terms of definitions of preferential entailment1 and assessed their correctness using his assessment framework.

[21] Full text  Patrick Doherty, Jonas Kvarnstr√∂m and Fredrik Heintz. 2009.
A Temporal Logic-based Planning and Execution Monitoring Framework for Unmanned Aircraft Systems.
Autonomous Agents and Multi-Agent Systems, 19(3):332–377. Springer.
DOI: 10.1007/s10458-009-9079-8.

Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.

[20] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2009.
Stream Reasoning in DyKnow: A Knowledge Processing Middleware System.
In Proceedings of the Stream Reasoning Workshop. In series: CEUR Workshop Proceedings #466. M. Jeusfeld c/o Redaktion Sun SITE, Informatik V, RWTH Aachen.

The information available to modern autonomous systems is often in the form of streams. As the number of sensors and other stream sources increases there is a growing need for incremental reasoning about the incomplete content of sets of streams in order to draw relevant conclusions and react to new situations as quickly as possible. To act rationally, autonomous agents often depend on high level reasoning components that require crisp, symbolic knowledge about the environment. Extensive processing at many levels of abstraction is required to generate such knowledge from noisy, incomplete and quantitative sensor data. We define knowledge processing middleware as a systematic approach to integrating and organizing such processing, and argue that connecting processing components with streams provides essential support for steady and timely flows of information. DyKnow is a concrete and implemented instantiation of such middleware, providing support for stream reasoning at several levels. First, the formal KPL language allows the specification of streams connecting knowledge processes and the required properties of such streams. Second, chronicle recognition incrementally detects complex events from streams of more primitive events. Third, complex metric temporal formulas can be incrementally evaluated over streams of states. DyKnow and the stream reasoning techniques are described and motivated in the context of a UAV traffic monitoring application.

[19] Full text  Fredrik Heintz and Jonas Kvarnstr√∂m. 2009.
Proceedings of the Swedish AI Society Workshop 2009.
Conference Proceedings. In series: Link√∂ping Electronic Conference Proceedings #35. Link√∂ping University Electronic Press, Link√∂pings universitet. 65 pages.
Link to Book: http://www.ep.liu.se/ecp/035/

[18] Full text  Martin Magnusson, Jonas Kvarnstr√∂m and Patrick Doherty. 2009.
Abductive Reasoning with Filtered Circumscription.
In Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change (NRAC). UTSePress. ISBN: 978-0-9802840-7-2.

For logical artificial intelligence to be truly useful,its methods must scale to problems of realistic size.An interruptible algorithm enables a logical agentto act in a timely manner to the best of its knowledge,given its reasoning so far. This seems necessaryto avoid analysis paralysis, trying to thinkof every potentiality, however unlikely, beforehand.These considerations prompt us to look for alternativereasoning mechanisms for filtered circumscription,a nonmonotonic reasoning formalism usede.g. by Temporal Action Logic and Event Calculus.We generalize Ginsberg’s circumscriptive theoremprover and describe an interruptible theoremprover based on abduction that has been used tounify planning and reasoning in a logical agent architecture.

[17] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2009.
A Stream-Based Hierarchical Anchoring Framework.
In Proceedings of the IEEE/RSJ International Conference on Intelligent RObots and Systems (IROS). IEEE conference proceedings. ISBN: 978-1-4244-3803-7.
DOI: 10.1109/IROS.2009.5354372.
IEEE Xplore: http://ieeexplore.ieee.org/stamp/stamp.j...

2008
[16] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2008.
Bridging the Sense-Reasoning Gap: DyKnow - A Middleware Component for Knowledge Processing.
In Martin Hulse and Manfred Hild, editors, IROS Workshop on Current Software Frameworks in Cognitive Robotics Integrating Different Computational Paradigms.
Note: No proceedings, but CD

Developing autonomous agents displaying rational and goal-directed behavior in a dynamic physical environment requires the integration of both sensing and reasoning components. Due to the different characteristics of these components there is a gap between sensing and reasoning. We believe that this gap can not be bridged in a single step with a single technique. Instead, it requires a more general approach to integrating components on many different levels of abstraction and organizing them in a structured and principled manner. In this paper we propose knowledge processing middleware as a systematic approach for organizing such processing. Desirable properties of such middleware are presented and motivated. We then go on to argue that a declarative streambased system is appropriate to provide the desired functionality. Finally, DyKnow, a concrete example of stream-based knowledge processing middleware that can be used to bridge the sense-reasoning gap, is presented. Different types of knowledge processes and components of the middleware are described and motivated in the context of a UAV traffic monitoring application.

[15] Full text  Jonas Kvarnstr√∂m, Fredrik Heintz and Patrick Doherty. 2008.
A Temporal Logic-Based Planning and Execution Monitoring System.
In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS). AAAI Press. ISBN: 978-1-57735-386-7, 978-1-57735-387-4.

As no plan can cover all possible contingencies, the ability to detect failures during plan execution is crucial to the robustness of any autonomous system operating in a dynamic and uncertain environment. In this paper we present a general planning and execution monitoring system where formulas in an expressive temporal logic specify the desired behavior of a system and its environment. A unified domain description for planning and monitoring provides a solid shared declarative semantics permitting the monitoring of both global and operator-specific conditions. During plan execution, an execution monitor subsystem detects violations of monitor formulas in a timely manner using a progression algorithm on incrementally generated partial logical models. The system has been integrated on a fully deployed autonomous unmanned aircraft system. Extensive empirical testing has been performed using a combination of actual flight tests and hardware-in-the-loop simulations in a number of different mission scenarios.

[14] Full text  Fredrik Heintz, Jonas Kvarnstr√∂m and Patrick Doherty. 2008.
Knowledge Processing Middleware.
In S. Carpin, I. Noda, E. Pagello, M. Reggiani and O. von Stryk, editors, Proceedings of the 1st International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), pages 147–158. In series: Lecture Notes in Computer Science #5325. Springer. ISBN: 978-3-540-89075-1 (print), 978-3-540-89076-8 (online).
DOI: 10.1007/978-3-540-89076-8_17.

Developing autonomous agents displaying rational and goal-directed behavior in a dynamic physical environment requires the integration of a great number of separate deliberative and reactive functionalities. This integration must be built on top of a solid foundation of data, information and knowledge having numerous origins, including quantitative sensors and qualitative knowledge databases. Processing is generally required on many levels of abstraction and includes refinement and fusion of noisy sensor data and symbolic reasoning. We propose the use of knowledge processing middleware as a systematic approach for organizing such processing. Desirable properties of such middleware are presented and motivated. We then argue that a declarative stream-based system is appropriate to provide the desired functionality. Different types of knowledge processes and components of the middleware are described and motivated in the context of a UAV traffic monitoring application. Finally DyKnow, a concrete example of stream-based knowledge processing middleware, is briefly described.

2005
[13] Full text  Jonas Kvarnstr√∂m. 2005.
TALplanner and other extensions to Temporal Action Logic.
PhD Thesis. In series: Link√∂ping Studies in Science and Technology. Dissertations #937. Link√∂pings universitet. 302 pages. ISBN: 91-85297-75-5.

Though the exact definition of the boundary between intelligent and non-intelligent artifacts has been a subject of much debate, one aspect of intelligence that many would deem essential is <em>deliberation</em>: Rather than reacting \"instinctively\" to its environment, an intelligent system should also be capable of <em>reasoning</em> about it, reasoning about the effects of actions performed by itself and others, and creating and executing plans, that is, determining which actions to perform in order to achieve certain goals. True deliberation is a complex topic, requiring support from several different sub-fields of artificial intelligence. The work presented in this thesis spans two of these partially overlapping fields, beginning with <em>reasoning about action and change</em> and eventually moving over towards <em>planning</em>.The qualification problem relates to the difficulties inherent in providing, for each action available to an agent, an exhaustive list of all qualifications to the action, that is, all the conditions that may prevent the action from being executed in the intended manner. The first contribution of this thesis is a framework for <em>modeling qualifications</em> in Temporal Action Logic (TAL).As research on reasoning about action and change proceeds, increasingly complex and interconnected domains are modeled in increasingly greater detail. Unless the resulting models are structured consistently and coherently, they will be prohibitively difficult to maintain. The second contribution is a framework for <em>structuring TAL domains</em> using object-oriented concepts.Finally, the second half of the thesis is dedicated to the task of <em>planning</em>. TLplan pioneered the idea of using domain-specific control knowledge in a temporal logic to constrain the search space of a forward-chaining planner. We develop a new planner called TALplanner, based on the same idea but with fundamental differences in the way the planner verifies that a plan satisfies control formulas. T ALplanner generates concurrent plans and can take resource constraints into account. The planner also applies several new automated domain analysis techniques to control formulas, further increasing performance by orders of magnitude for many problem domains.

2004
[12] Full text  Joakim Gustafsson and Jonas Kvarnstr√∂m. 2004.
Elaboration tolerance through object-orientation.
Artificial Intelligence, 153(1-2):239–285. Elsevier.
DOI: 10.1016/j.artint.2003.08.004.

Although many formalisms for reasoning about action and change have been proposed in the literature, any concrete examples provided in such articles have primarily consisted of tiny domains that highlight some particular aspect or problem. However, since some of the classical problems are now completely or partially solved and since powerful tools are becoming available, it is now necessary to start modeling more complex domains. This article presents a methodology for handling such domains in a systematic manner using an object-oriented framework and provides several examples of the elaboration tolerance exhibited by the resulting models. (C) 2003 Elsevier B.V. All rights reserved.

2003
[11] Full text  Jonas Kvarnstr√∂m and Martin Magnusson. 2003.
TALplanner in the Third International Planning Competition: Extensions and control rules.
The journal of artificial intelligence research, 20(??):343–377. AAAI Press.
DOI: 10.1613/jair.1189.

TALplanner is a forward-chaining planner that relies on domain knowledge in the shape of temporal logic formulas in order to prune irrelevant parts of the search space. TALplanner recently participated in the third International Planning Competition, which had a clear emphasis on increasing the complexity of the problem domains being used as benchmark tests and the expressivity required to represent these domains in a planning system. Like many other planners, TALplanner had support for some but not all aspects of this increase in expressivity, and a number of changes to the planner were required. After a short introduction to TALplanner, this article describes some of the changes that were made before and during the competition. We also describe the process of introducing suitable domain knowledge for several of the competition domains.

2002
[10] Full text  Jonas Kvarnstr√∂m. 2002.
Applying Domain Analysis Techniques for Domain-Dependent Control in TALplanner.
In Malik Ghallab, Joachim Hertzberg, and Paolo Traverso, editors, Proceedings of the 6th International Conference on Artificial Intelligence Planning and Scheduling (AIPS). AAAI Press. ISBN: 0-57735-142-8.
DOI: 10.3233/978-1-60750-606-5-341.

A number of current planners make use of automatic domain analysis techniques to extract information such as state invariants or necessary goal orderings from a planning domain. There are also planners that allow the user to explicitly specify additional information intended to improve performance. One such planner is TALplanner, which allows the use of domain-dependent temporal control formulas for pruning a forward-chaining search tree. This leads to the question of how these two approaches can be combined. In this paper we show how to make use of automatically generated state invariants to improve the performance of testing control formulas. We also develop a new technique for analyzing control rules relative to control formulas and show how this often allows the planner to automatically strengthen the preconditions of the operators, thereby reducing time complexity and improving the performance of TALplanner by a factor of up to 400 for the largest problems from the AIPS-2000 competition.

2001
[9] Full text  Joakim Gustafsson and Jonas Kvarnstr√∂m. 2001.
Elaboration Tolerance through Object-Orientation.
In Proceedings of the 5th Symposium on Logical Formalizations of Commonsense Reasoning (CommonSense).

Although many formalisms for reasoning about action and change have been proposed in the literature, their semantic adequacy has primarily been tested using tiny domains that highlight some particular aspect or problem. However, since some of the classical problems are completely or partially solved and since powerful tools are available, it is now necessary to start modeling more complex domains. This paper presents a methodology for handling such domains in a systematic manner using an object-oriented framework and provides several examples of the elaboration tolerance exhibited by the resulting models.

[8] Full text  Patrick Doherty and Jonas Kvarnstr√∂m. 2001.
TALPLANNER - A temporal logic-based planner.
The AI Magazine, 22(3):95–102. AAAI Press.

TALPLANNER is a forward-chaining planner that utilizes domain-dependent knowledge to control search in the state space generated by action invocation. The domain-dependent control knowledge, background knowledge, plans, and goals are all represented, using,formulas in, a temporal logic called TAL, which has been developed independently as a formalism for specifying agent narratives and reasoning about them. In the Fifth International Artificial Intelligence Planning and Scheduling Conference planning competition, TALPLANNER exhibited impressive performance, winning the Outstanding Performance Award in the Domain-Dependent Planning Competition. In this article, we provide an overview of TALPLANNER.

2000
[7] Full text  Patrick Doherty and Jonas Kvarnstr√∂m. 2000.
TALplanner: A temporal logic based forward chaining planner.
Annals of Mathematics and Artificial Intelligence, 30(1-4):119–169. Springer.
DOI: 10.1023/A:1016619613658.

We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal and control formulas. The sequential version of TALplanner is presented. The expressivity of plan operators is then extended to deal with an interesting class of resource types. An algorithm for generating concurrent plans, where operators have varying durations and internal state, is also presented. All versions of TALplanner have been implemented. The potential of these techniques is demonstrated by applying TALplanner to a number of standard planning benchmarks in the literature.

[6] Full text  Jonas Kvarnstr√∂m and Patrick Doherty. 2000.
Tackling the qualification problem using fluent dependency constraints.
Computational intelligence, 16(2):169–209. Blackwell Publishing.
DOI: 10.1111/0824-7935.00111.

In the area of formal reasoning about action and change, one of the fundamental representation problems is providing concise modular and incremental specifications of action types and world models, where instantiations of action types are invoked by agents such as mobile robots. Provided the preconditions to the action are true, their invocation results in changes to the world model concomitant with the goal-directed behavior of the agent. One particularly difficult class of related problems, collectively called the qualification problem, deals with the need to find a concise incremental and modular means of characterizing the plethora of exceptional conditions that might qualify an action, but generally do not, without having to explicitly enumerate them in the preconditions to an action. We show how fluent dependency constraints together with the use of durational fluents can be used to deal with problems associated with action qualification using a temporal logic for action and change called TAL-Q. We demonstrate the approach using action scenarios that combine solutions to the frame, ramification, and qualification problems in the context of actions with duration, concurrent actions, nondeterministic actions, and the use of both Boolean and non-Boolean fluents. The circumscription policy used for the combined problems is reducible to the first-order case.

[5] Full text  Jonas Kvarnstr√∂m, Patrick Doherty and Patrik Haslum. 2000.
Extending TALplanner with concurrency and resources.
In Proceedings of the 14th European Conference on Artificial Intelligence (ECAI). In series: Frontiers in Artificial Intelligence and Applications #54. IOS Press. ISBN: 1586030132.

We present TALplanner, a forward-chaining planner based on the use of domain-dependent search control knowledge represented as temporal formulas in the Temporal Action Logic (TAL). TAL is a narrative based linear metric time logic used for reasoning about action and change in incompletely specified dynamic environments. TAL is used as the formal semantic basis for TALplanner, where a TAL goal narrative with control formulas is input to TALplanner which then generates a TAL narrative that entails the goal formula. We extend the sequential version of TALplanner, which has previously shown impressive performance on standard benchmarks, in two respects: 1) TALplanner is extended to generate concurrent plans, where operators have varied durations and internal state; and 2) the expressiveness of plan operators is extended for dealing with several different types of resources. The extensions to the planner have been implemented and concurrent planning with resources is demonstrated using an extended logistics benchmark.

1999
[4] Full text  Patrick Doherty and Jonas Kvarnstr√∂m. 1999.
TALplanner: An empirical investigation of a temporal logic-based forward chaining planner.
In Clare Dixon, Michael Fisher, editors, 6th International Workshop on Temporal Representation and Reasoning (TIME-99). IEEE Computer Society. ISBN: 0-7695-0173-7.

We present a new forward chaining planner, TALplanner, based on ideas developed by Bacchus and Kabanza, where domain-dependent search control knowledge represented as temporal formulas is used to effectively control forward chaining. Instead of using a linear modal tense logic as with Bacchus and Kabanza, we use TAL, a narrative-based linear temporal logic used for reasoning about action and change in incompletely specified dynamic environments. Two versions of TALplanner are considered, TALplan/modal which is based on the use of emulated modal formulas and a progression algorithm, and TALplan/non-modal which uses neither modal formulas nor a progression algorithm. For both versions of TALplanner and for all tested domains, TALplanner is shown to be considerably faster and requires less memory. The TAL versions also permit the representation of durative actions with internal state.

1998
[3] Full text  Patrick Doherty, Joakim Gustafsson, Lars Karlsson and Jonas Kvarnstr√∂m. 1998.
(TAL) temporal action logics: Language specification and tutorial.
Electronic Transactions on Artifical Intelligence, 2(3-4):273–306.
Link: http://www.ep.liu.se/ej/etai/1998/009/

The purpose of this article is to provide a uniform, lightweight language specication and tutorial for a class of temporal logics for reasoning about action and change that has been developed by our group during the period 1994-1998. The class of logics are collected under the name TAL, an acronym for Temporal Action Logics. TAL has its origins and inspiration in the work with Features and Fluents (FF) by Sandewall, but has diverged from the methodology and approach through the years. We first discuss distinctions and compatibility with FF, move on to the lightweight language specication, and then present a tutorial in terms of an excursion through the different parts of a relatively complex narrative defined using TAL. We conclude with an annotated list of published work from our group. The article tries to strike a reasonable balance between detail and readability, making a number of simplications regarding narrative syntax and translation to a base logical language. Full details are available in numerous technical reports and articles which are listed in the final section of this article.

[2] Full text  Patrick Doherty and Jonas Kvarnstr√∂m. 1998.
Tackling the qualification problem using fluent dependency constraints.
In Lina Khatib, Robert Morris, editors, Proceedings of the 5th International Workshop on Temporal Representation and Reasoning (TIME-98). IEEE Computer Society. ISBN: 0-8186-8473-9.
Note: Preliminary report

The use of causal rules or fluent dependency constraints has proven to provide a versatile means of dealing with the ramification problem. In this paper we show how fluent dependency constraints together with the use of durational fluents can be used to deal with problems associated with action qualification. We provide both a \emph{weak} and \emph{strong} form of qualification and demonstrate the approach using an action scenario which combines solutions to the frame, ramification and qualification problems in the context of actions with duration, concurrent actions, non-deterministic actions and the use of both boolean and non-boolean fluents. The circumscription policy used for the combined problems is reducible to the 1st-order case. In addition, we demonstrate the use of a research tool VITAL, for querying and visualizing action scenarios.

1996
[1] Jonas Kvarnstr√∂m. 1996.
A New Tractable Planner for the SAS+ Formalism.
Student Thesis. In series: LiTH-IDA-Ex #9625. 283 pages. ISRN: LiTH-IDA-Ex-9625.

Computer science