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.

Temporal formalisms are essential for reasoning about actions that are carried out over time. The exact durations of such actions are generally hard to predict. In temporal planning, the resulting uncertainty is often worked around by only considering upper bounds on durations, with the assumption that when 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. Using <em>Simple Temporal Networks with Uncertainty (STNU)</em>, a planner can correctly take both lower and upper duration bounds into account. It must then verify that the plans it generates are executable regardless of the actual outcomes of the uncertain durations. This is captured by the property of <em>dynamic controllability</em> (DC), which should be verified incrementally during plan generation. Recently a new incremental algorithm for verifying dynamic controllability was proposed: <em>EfficiantIDC</em>, which can verify if an STNU that is DC remains DC after the addition or tightening of a constraint (corresponding to a new action being added to a plan). The algorithm was shown to have a worst case complexity of <em>O</em>(n⁴) for each addition or tightening. This can be amortized over the construction of a whole STNU for an amortized complexity in <em>O</em>(n³). In this paper we improve the <em>EfficientIDC</em> algorithm in a way that prevents it from having to reprocess nodes. This improvement leads to a lower worst case complexity in <em>O</em>(n³).

This article presents a novel framework for modeling the troubleshooting process for automotive systems such as trucks and buses. We describe how a diagnostic model of the troubleshooting process can be created using event-driven, nonstationary, dynamic Bayesian networks. Exact inference in such a model is in general not practically possible. Therefore, we evaluate different approximate methods for inference based on the Boyen–Koller algorithm. We identify relevant model classes that have particular structure such that inference can be made with linear time complexity. We also show how models created using expert knowledge can be tuned using statistical data. The proposed learning mechanism can use data that is collected from a heterogeneous fleet of modular vehicles that can consist of different components. The proposed framework is evaluated both theoretically and experimentally on an application example of a fuel injection system.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.