We extend the answer set semantics to a class of logic programs with nested expressions permitted in the bodies and heads of rules. These expressions are formed from literals using negation as failure, conjunction (,) and disjunction (;) that can be nested arbitrarily. Conditional expressions are introduced as abbreviations. The study of equivalent transformations of programs with nested expressions shows that any such program is equivalent to a set of disjunctive rules, possibly with negation as failure in the heads. The generalized answer set semantics is related to the LloydTopor generalization of Clark's completion and to the logic of minimal belief and negation as failure.

The paper discusses an architecture for intelligent agents based on the use of A-Prolog - a language of logic programs under the answer set semantics. A-Prolog is used to represent the agent's knowledge about the domain and to formulate the agent's reasoning tasks. We outline how these tasks can be reduced to answering questions about properties of simple logic programs and demonstrate the methodology of constructing these programs. Keywords: Intelligent agents, logic programming and nonmonotonic reasoning. 1 INTRODUCTION This paper is a report on the attempt by the authors to better understand the design of software components of intelligent agents capable of reasoning, planning and acting in a changing environment. The class of such agents includes, but is not limited to, intelligent mobile robots, softbots, immobots, intelligent information systems, expert systems, and decision-making systems. The ability to design intelligent agents (IA) is crucial for such diverse tasks as ...

The goal of this paper is to test if a programming methodology based on the declarative language A-Prolog, algorithms for computing answer sets of programs of A-Prolog, and programming systems implementing these algorithms can be successfully applied to the development of medium size knowledge-intensive applications. We report on a successful design and development of such a system controlling some of the functions of the Space Shuttle. Introduction The research presented in this paper is rooted in recent developments in several areas of AI. Advances in the work on semantics of negation in logic programming (Gelfond & Lifschitz 1988; 1991) and on formalization of common-sense reasoning (Reiter 1980; Moore 1985) led to the development of the declarative language, A-Prolog, used in this paper to encode the domain knowledge, and to an A-Prolog based methodology for representing defaults. Insights on the nature of causality and its relationship with answer sets of logic programs (...

In this paper we suggest an architecture for a software agent which operates a physical device and is capable of making observations and of testing and repairing the device components. We present novel de nitions of the notions of symptom, candidate diagnosis, and diagnosis which are based on the theory of action language AL. The new de nitions allow one to give a simple account of the agent's behavior in which many of the agent's tasks are reduced to computing stable models of logic programs.

. We propose a new logic-based planning language, called K. Transitions between states of knowledge can be described in K, and the language is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. A planning system supporting K is implemented on top of the disjunctive logic programming system DLV. This novel system allows for solving hard planning problems, including secure planning under incomplete initial states, which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. 1 Introduction The need for modeling the behavior of robots in a formal way led to the definition of logic-based languages for reasoning about actions and action planning, such as [24, 8, 15, 10, 34, 11, 19, 12, 14]. These languages allow us to specify planning problems of the form ...

In recent research on nonmonotonic logic programming, repeatedly strong equivalence of logic programs P and Q has been considered, which holds if the programs P ∪ R and Q ∪ R have the same answer sets for any other program R. This property strengthens the equivalence of P and Q with respect to answer sets (which is the particular case for R =∅), and has its applications in program optimization, verification, and modular logic programming. In this article, we consider more liberal notions of strong equivalence, in which the actual form of R may be syntactically restricted. On the one hand, we consider uniform equivalence where R is a set of facts, rather than a set of rules. This notion, which is well-known in the area of deductive databases, is particularly useful for assessing whether programs P and Q are equivalent as components of a logic program which is modularly structured. On the other hand, we consider relativized notions of equivalence where R ranges over rules over a fixed alphabet, and thus generalize our results to relativized notions of strong and uniform equivalence. For all these notions, we consider disjunctive logic programs in the propositional (ground) case as well as some restricted classes, providing semantical characterizations and analyzing the computational complexity. Our results, which naturally extend to answer set semantics for programs with strong negation, complement the results on strong

In this paper we apply computer-aided theorem discovery technique to discover theorems about strongly equivalent logic programs under the answer set semantics. Our discovered theorems capture new classes of strongly equivalent logic programs that can lead to new program simplification rules that preserve strong equivalence. Specifically, with the help of computers, we discovered exact conditions that capture the strong equivalence between a rule and the empty set, between two rules, between two rules and one of the two rules, between two rules and another rule, and between three rules and two of the three rules. 1.

In this paper we give a brief introduction to the declarative knowledge representation and logic programming language A-Prolog. We demonstrate the methodology of programming in A-Prolog by developing a simple declarative program describing dynamic behavior of combinational digital circuits. The implementation is proven to be correct and is supplied with a graphical interface which facilitates the use by students. Our experiment conrms our belief that A-Prolog can become a language of choice for various knowledge intensive applications. 1. Introduction It is becoming increasingly clear that to fully realize the potential of the computer revolution, computer scientists must develop a systematic methodology for design and construction of software systems capable of basing their behavior on knowledge about their environment. Without such methodology we can create neither autonomous robots nor intelligent information, expert, and decision making systems. This realization led to work on d...

This paper presents a new system called ProLogICA for Abductive Logic Programming (ALP) with Negation as Failure (NAF) and Integrity Constraints (ICs). The system builds upon existing ALP techniques but includes several optimisations and extensions necessitated by recent applications in computational biology, temporal reasoning and machine learning. Unlike some other ALP systems that support non-ground abduction through the integrated use of constraint solving, we adopt a more lightweight approach which avoids this complexity at the expense of only computing ground hypotheses. We argue our approach is suited to a wide class of real-world problems and demonstrate the effectiveness of ProLogICA on three non-trivial applications.

In this work we discuss planning performed by the USAAdvisor decision support system for the Space Shuttle. The USA-Advisor is a medium size, planning application for use by NASA flight controllers. This system contains over a dozen domain dependent and domain independent heuristics.