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.

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.

We consider a constrained optimization problem with mixed integer and real variables. It models optimal placement of communications relay nodes in the presence of obstacles. This problem is widely encountered, for instance, in robotics, where it is required to survey some target located in one point and convey the gathered information back to a base station located in another point. One or more unmanned aerial or ground vehicles (UAVs or UGVs) can be used for this purpose as communications relays. The decision variables are the number of unmanned vehicles (UVs) and the UV positions. The objective function is assumed to access the placement quality. We suggest one instance of such a function which is more suitable for accessing UAV placement. The constraints are determined by, firstly, a free line of sight requirement for every consecutive pair in the chain and, secondly, a limited communication range. Because of these requirements, our constrained optimization problem is a difficult multi-extremal problem for any fixed number of UVs. Moreover, the feasible set of real variables is typically disjoint. We present an approach that allows us to efficiently find a practically acceptable approximation to a global minimum in the problem of optimal placement of communications relay nodes. It is based on a spatial discretization with a subsequent reduction to a shortest path problem. The case of a restricted number of available UVs is also considered here. We introduce two label correcting algorithms which are able to take advantage of using some peculiarities of the resulting restricted shortest path problem. The algorithms produce a Pareto solution to the two-objective problem of minimizing the path cost and the number of hops. We justify their correctness. The presented results of numerical 3D experiments show that our algorithms are superior to the conventional Bellman-Ford algorithm tailored to solving this problem.

he National Conference on Artificial Intelligence remains the bellwether for research in artificial intelligence. Leading AI researchers and practitioners as well as scientists and engineers in related fields present theoretical, experimental, and empirical results, covering a broad range of topics that include principles of cognition, perception, and action; the design, application, and evaluation of AI algorithms and systems; architectures and frameworks for classes of AI systems; and analyses of tasks and domains in which intelligent systems perform. The Innovative Applications of Artificial Intelligence conference highlights successful applications of AI technology; explores issues, methods, and lessons learned in the development and deployment of AI applications; and promotes an interchange of ideas between basic and applied AI. This volume presents the proceedings of the latest conferences, held in July, 2006.

The purpose of this article is to provide a uniform, lightweight language specification 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 specification, 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 simplifications 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.

We propose an approach to modeling delayed effects of actions which is based on the use of causal constraints and their interaction with the direct effects of actions. The approach extends previous work with a causal approach used to deal with the ramification problem. We show the similarity between solutions to the modeling of indirect effects and delayed effects of actions by example. The base logic PMON+ is a temporal logic for reasoning about action and change and uses circumscription. It is shown that the extension for delayed effects of actions retains the first-order reducibility property shown previously for successfully dealing with the frame and ramification problems for a large class of action scenarios. We also consider the “causal qualification” problem, \"natural death\" of fluents and causal lag, each of which is closely related to the use of delayed effects.

<strong></strong> We propose an approach to modeling delayed effects of actions which is based on the use of causal constraints and their interaction with the direct effects of actions. The approach extends previous work with a causal approach used to deal with the ramification problem. We show the similarity between solutions to the modeling of indirect effects and delayed effects of actions by example. The base logic PMON+ is a temporal logic for reasoning about action and change and uses circumscription. It is shown that the extension for delayed effects of actions retains the first-order reducibility property shown previously for successfully dealing with the frame and ramification problems for a large class of action scenarios. We also consider the “causal qualification” problem, \"natural death\" of fluents and causal lag, each of which is closely related to the use of delayed effects.

This report describes the current state of work with PMON, a logic for reasoning about actions and change, and its extensions. PMON has been assessed correct for the K-IA class using Sandewall's Features and Fluents framework which provides tools for assessing the correctness of logics of action and change. A syntactic characterization of PMON has previously been provided in terms of a circumscription axiom which is shown to be reducible to a first-order formula. This report introduces a number of new extensions which are also reducible and deal with ramification. This report is intended to provide a formal specification of the PMON family of logics and the surface language L(SD) used to represent action scenario descriptions. It should be considered a working draft. The title of the report has a version number because both the languages and logics are continually evolving. Since this document is intended as a formal specification which is used by our group as a reference for research and implementation, it is understandably brief as regards intuitions and applications of the language and logics defined. We do provide a set of benchmarks and comments concerning these which can serve as a means of comparing this formalism with others. The set of benchmarks is not complete and is only intended to provide representative examples of the expressivity and use of this particular family of logics. We describe its features and limitations in other publications by our group which can normally be found at http://www.ida.liu.se/labs/kplab/.

Recently, a great deal of progress has been made using nonmonotonic temporal logics to formalize reasoning about action and change. In particular, much focus has been placed on the proper representation of non-deterministic actions and the indirect effects of actions. One popular approach to representing the indirect effects of actions has been via the use of causal rules which in a more general sense can be viewed as fluent dependency constraints. Although fluent dependency constraints have been used primarily under a loose causal interpretation, we show that when interpreted in a broader sense they provide a flexible means for dealing with a number of other representational problems such as the qualification problem and the ramification constraints as qualification constraints problem, in addition to the standard ramification problem. More importantly, the use of fluent dependency constraints for different purposes does not involve additions to the base nonmonotonic temporal logic, TAL, used here, but simply the addition of several macro operators to an action language used to represent action scenarios or narratives. The payoff is that TAL has already been shown to offer a robust approach to representing action scenarios which permit incomplete specifications of both state and the timing of actions, non-deterministic actions, actions with duration, concurrent actions, use of both boolean and non-boolean fluents, and solutions to the frame and ramification problems for a wide class of action scenarios. In addition, all circumscribed action scenarios in these classes and the more general class involving qualification considered in this paper can be shown to be reducible to the first-order case. Finally, a restricted entailment method for this new class of scenarios is fully implemented. In the paper, we present a challenge example which incorporates all these features, propose a distinction between weak and strong qualification with a representation of both, and provide a visualization of the preferred entailments using a research tool VITAL for querying and visualizing action scenarios.

This report describes the current state of work with PMON, a logic for reasoning about action and change, and its extensions. PMON has been assessed correct for the K-IA class using Sandewall's Features and Fluents framework which provides tools for assessing the correctness of logics of action and change. A syntactic characterization of PMON has previously been provided in terms of a circumscription axiom which is shown to be reducible to a first-order formula. This report introduces a number of new extensions which are also reducible and deal with ramification. The report is intended to provide a formal specification for the PMON family of logics and the surface language L(SD) used to represent action scenario descriptions. It should be considered a working draft. The title of the report has a version number because both the languages and logics used are continually evolving. Since this document is intended as a formal specification which is used by our group as a reference for research and implementation, it is understandably brief as regards intuitions and applications of the languages and logics defined. We do provide a set of benchmarks and comments concerning these which can serve as a means of comparing this formalism with others. The set of benchmarks is not complete and is only intended to provide representative examples of the expressivity and use of this particular family of logics. We describe its features and limitations in other publications by our group which can normally be found at \"http://www.ida.liu.se/labs/kplab/\".

In this paper, we propose a new way of considering reasoning about action and change. Rather than placing a preferential structure onto the models of logical theories, we place such a structure directly on the semantics of the actions involved. In this way, we obtain a preferential semantics of actions by means of which we can not only deal with several of the traditional problems in this area such as the frame and ramification problems, but can generalize these solutions to a context which includes both nondeterministic and concurrent actions. In fact, the net result is an integration of semantical and verificational techniques from the paradigm of imperative and concurrent programs in particular, as known from traditional programming, with the AI perspective. In this paper, the main focus is on semantical (i.e. model theoretical) issues rather than providing a logical calculus, which would be the next step in the endeavor.

In recent years, a great deal of attention has been devoted to logics of \"commonsense\" reasoning. Among the candidates proposed, circumscription has been perceived as an elegant mathematical technique for modeling nonmonotonic reasoning, but difficult to apply in practice. The major reason for this is the nd-order nature of circumscription axioms and the difficulty in finding proper substitutions of predicate expressions for predicate variables. One solution to this problem is to compile, where possible, nd-order formulas into equivalent 1st-order formulas. Although some progress has been made using this approach, the results are not as strong as one might desire and they are isolated in nature. In this article, we provide a general method which can be used in an algorithmic manner to reduce circumscription axioms to 1st-order formulas. The algorithm takes as input an arbitrary 2nd-order formula and either returns as output an equivalent 1st-order formula, or terminates with failure. The class of 2nd-order formulas, and analogously the class of circumscriptive theories which can be reduced, provably subsumes those covered by existing results. We demonstrate the generality of the algorithm using circumscriptive theories with mixed quantifiers (some involving Skolemization), variable constants, non-separated formulas, and formulas with n-ary predicate variables. In addition, we analyze the strength of the algorithm and compare it with existing approaches providing formal subsumption results.

In this paper, we consider the problem of partitioning a polygon into a set of connected disjoint sub-polygons, each of which covers an area of a specific size. The work is motivated by terrain covering applications in robotics, where the goal is to find a set of efficient plans for a team of heterogeneous robots to cover a given area. Within this application, solving a polygon partitioning problem is an essential stepping stone. Unlike previous work, the problem formulation proposed in this paper also considers a compactness metric of the generated sub-polygons, in addition to the area size constraints. Maximizing the compactness of sub-polygons directly influences the optimality of any generated motion plans. Consequently, this increases the efficiency with which robotic tasks can be performed within each sub-region. The proposed problem representation is based on grid cell decomposition and a potential field model that allows for the use of standard optimization techniques. A new algorithm, the AreaDecompose algorithm, is proposed to solve this problem. The algorithm includes a number of existing and new optimization techniques combined with two post-processing methods. The approach has been evaluated on a set of randomly generated polygons which are then divided using different criteria and the results have been compared with a state-of-the-art algorithm. Results show that the proposed algorithm can efficiently divide polygon regions maximizing compactness of the resulting partitions, where the sub-polygon regions are on average up to 73% more compact in comparison to existing techniques.