The paper presents a pair of new conformant planners, CPA pc and CPA ph, based on recent developments in theory of action and change. As an input the planners take a domain description D in action language AL which allows state constraints (non-stratified axioms), together with a set of CNF formulae describing the initial state, and a set of literals representing the goal. We propose two approximations of the transition diagram T defined by D. Both approximations are deterministic transition functions and can be computed efficiently. Moreover they are sound (and sometimes complete) with respect to T. In its search for a plan, an approximation based planner analyses paths of an approximation instead of that of T. CPA pc and CPA ph are forward, best first search planners based on this idea. We compare them with two stateof-the-art conformant planners, KACMBP and Conformant-FF (CFF), over benchmarks in the literature, and over two new domains. One has large number of state constraints and another has a high degree of incompleteness. Our planners perform reasonably well in benchmark domains and outperform KACMBP and CFF in the first domain while still working well with the second one. Our experimental result shows that having an integral part of a conformant planner to deal with state constraints directly can significantly improve its performance, extending a similar claim for classical planners

Abstract. In this paper, we describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition; and, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter.

Abstract. We describe a novel approach to tackle intention recognition, by combining dynamically configurable and situation-sensitive Causal Bayes Networks plus plan generation techniques. Given some situation, such networks enable the recognizing agent to come up with the most likely intentions of the intending agent, i.e. solve one main issue of intention recognition. And, in case of having to make a quick decision, focus on the most important ones. Furthermore, the combination with plan generation provides a significant method to guide the recognition process with respect to hidden actions and unobservable effects, in order to confirm or disconfirm likely intentions. The absence of this articulation is a main drawback of the approaches using Bayes Networks solely, due to the combinatorial problem they encounter. We explore and exemplify its application, in the Elder Care context, of the ability to perform Intention Recognition and of wielding Evolution Prospection methods to help the Elder achieve its intentions. This is achieved by means of an articulate use of a Causal Bayes Network to heuristically gauge probable general intention – combined with specific generation of plans involving preferences – for checking which such intentions are plausibly being carried out in the specific situation at hand, and suggesting actions to the Elder. The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example.

Abstract. In this paper we generalize the notion of approximation of action theories introduced in [13,26]. We introduce a logic programming based method for constructing approximation of action theories of AL and prove its soundness. We describe an approximation based conformant planner and compare its performance with other state-of-the-art conformant planners. 1

Abstract. We provide new perspectives on the semantics of logic programs with constraints. To this end we introduce several notions of computation and propose to use the results of computations as answer sets of programs with constraints. We discuss the rationale behind different classes of computations and study the relationships among them and among the corresponding concepts of answer sets. The proposed semantics generalize the answer set semantics for programs with monotone, convex and/or arbitrary constraints described in the literature. 1

Abstract. We explore and exemplify the application in the Elder Care context of the ability to perform Intention Recognition and of wielding Evolution Prospection methods. This is achieved by means of an articulate use of Causal Bayes Nets (for heuristically gauging probable general intentions), combined with specific generation of plans involving preferences (for checking which such intentions are plausibly being carried out in the specific situation at hand). The overall approach is formulated within one coherent and general logic programming framework and implemented system. The paper recaps required background and illustrates the approach via an extended application example.

Abstract. The paper describes an experiment of answer set planning in biochemical pathway planning. The focus is on large planning problem instances. It is shown that well-known planning techniques, such as planning graph analysis, landmarks recognition, and planning using landmarks are useful in answer set planning and can be easily incorporated in an answer set planning system. 1

Abstract. In this paper we generalize the notion of approximation of action theories introduced in [13, 26]. We introduce a logic programming based method for constructing approximation of action theories of �� � and prove its soundness. We describe an approximation based conformant planner and compare its performance with other state-of-the-art conformant planners. 1

The paper presents a calculus based on resolution for credulous reasoning in Answer Set Programming. The new approach allows a top-down and goal directed resolution, in the same spirit as traditional SLD-resolution. The proposed credulous resolution can be used in query-answering with nonground queries and with non-ground, and possibly infinite, programs. Soundness and completeness results for the resolution procedure are proved for large classes of logic programs. The resolution procedure is also extended to handle some traditional syntactic extensions used in Answer Set Programming, such as choice rules and constraints. The paper also describes an initial implementation of a system for credulous reasoning in Answer Set Programming.

In this paper we extend the logic programming based conformant planner described in [Son et al., 2005a] to allow it to work on planning problems with more complex descriptions of the initial states. We also compare the extended planner with other concurrent conformant planners. 1