Disjunctive Logic Programming (DLP) is an advanced formalism for knowledge representation and reasoning, which is very expressive in a precise mathematical sense: it allows to express every property of finite structures that is decidable in the complexity class ΣP 2 (NPNP). Thus, under widely believed assumptions, DLP is strictly more expressive than normal (disjunction-free) logic programming, whose expressiveness is limited to properties decidable in NP. Importantly, apart from enlarging the class of applications which can be encoded in the language, disjunction often allows for representing problems of lower complexity in a simpler and more natural fashion. This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to ∆P 3-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of

In classical planning, the planner is given a concrete goal; it returns a plan for it or a failure message. In the latter case, the user can either quit or modify the goal. For many applications, it is more convenient to let the user provide a more elaborate specification consisting of constraints and preferences over possible goal states. Then, let the system discover a plan for the most desirable among the feasible goal states. To materialize such an approach we require a formalism for specifying preferences and constraints over goals and an algorithm for solving the resulting constrained optimization problem. In this work we motivate the need for planning with preferences and constraints, suggest a rich, yet intuitive formalism for representing goal preferences in the context of a deterministic action model, discuss some of its properties, propose an efficient algorithm for planning with preferences and constraints based on this formalism, and provide extensive experimental analysis in an interesting new domain of configuration planning.

The aim of this paper is to demonstrate that A-Prolog is a powerful language for the construction of reasoning systems. In fact, A-Prolog allows to specify the initial situation, the domain model, the control knowledge, and the reasoning modules. Moreover, it is efficient enough to be used for practical tasks and can be nicely integrated with programming languages such as Java. An extension of A-Prolog (CR-Prolog) allows to further improve the quality of reasoning by specifying requirements that the solutions should satisfy if at all possible. The features of A-Prolog and CR-Prolog are demonstrated by describing in detail the design of USA-Advisor, an A-Prolog based decision support system for the Space Shuttle flight controllers.

In this paper we show how CR-Prolog, a recent extension of A-Prolog, was used in the successor of USA-Advisor (USA-Smart) in order to improve the quality of the plans returned. The general problem that we address is that of improving the quality of plans by taking in consideration statements that describe "most desirable" plans. We believe that USA-Smart proves that CR-Prolog provides a simple, elegant, and flexible solution to this problem, and can be easily applied to any planning domain. We also discuss how alternative extensions of A-Prolog can be used to obtain similar results.

We address the issue of incorporating domain-specific preferences in planning systems, where a preference may be seen as a "soft" constraint that it is desirable, but not necessary, to satisfy. To this end

preference description language PDL . This language allows us to combine qualitative and quantitative, penalty based preferences in a flexible way. This makes it possible to express complex preferences which are needed in many realistic optimization settings. We show that several preference handling methods described in the literature are special cases of our approach. We also demonstrate that PDL expressions can be compiled to logic programs which can be used as tester programs in a generate-and-improve method for finding optimal answer sets.

We present an agent monitoring approach, which aims at refuting from (possibly incomplete) information at hand that a multi-agent system (MAS) is implemented properly. In this approach, agent collaboration is abstractly described in an action theory. Action sequences reaching the collaboration goal are determined by a planner, whose compliance with the actual MAS behavior allows to detect possible collaboration failures. The approach can be fruitfully applied to aid offline testing of a MAS implementation, as well as online monitoring.

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

One of the major reasons for the success of answer set programming in recent years was the shift from a theorem proving to a constraint programming view: problems are represented such that stable models, respectively answer sets, rather than theorems correspond to solutions. This shift in perspective proved extremely fruitful in many areas. We believe that going one step further from a "hard" to a "soft" constraint programming paradigm, or, in other words, to a paradigm of qualitative optimization, will prove equally fruitful. In this paper we try to support this claim by showing that several generic problems in logic based problem solving can be understood as qualitative optimization problems, and that these problems have simple and elegant formulations given adequate optimization constructs in the knowledge representation language.

We consider the problem of representing arbitrary preferences in causal reasoning and planning systems. In planning, a preference may be seen as a goal or constraint that is desirable, but not necessary, to satisfy. To begin, we define a very general query language for histories, or interleaved sequences of world states and actions. Based on this, we specify a second language in which preferences are defined. A single preference defines a binary relation on histories, indicating that one history is preferred to the other. ¿From this, one can define global preference orderings on the set of histories, the maximal elements of which are the preferred histories. The approach is very general and flexible; thus it constitutes a “base” language in terms of which higher-level preferences may be defined. To this end, we investigate two fundamental types of preferences that we call choice and temporal preferences. We consider concrete strategies for these types of preferences and encode them in terms of our framework. We suggest how to express aggregates in the approach, allowing, for example, the expression of a preference for histories with lowest total action costs. Last, our approach can be used to express other approaches, and so serves as a common framework in which such