In this paper, we review some recent work on declarative logic programming languages based on stable models/answer sets semantics of logic programs. These languages, gathered together under the name of A-Prolog, can be used to represent various types of knowledge about the world. By way of example we demonstrate how the corresponding representations together with inference mechanisms associated with A-Prolog can be used to solve various programming tasks.

The recent approaches of extending the GRAPHPLAN algorithm to handle more expressive planning formalisms raise the question of what the formal meaning of “expressive power ” is. We formalize the intuition that expressive power is a measure of how concisely planning domains and plans can be expressed in a particular formalism by introducing the notion of “compilation schemes ” between planning formalisms. Using this notion, we analyze the expressiveness of a large family of propositional planning formalisms, ranging from basic STRIPS to a formalism with conditional effects, partial state specifications, and propositional formulae in the preconditions. One of the results is that conditional effects cannot be compiled away if plan size should grow only linearly but can be compiled away if we allow for polynomial growth of the resulting plans. This result confirms that the recently proposed extensions to the GRAPHPLAN algorithm concerning conditional effects are optimal with respect to the “compilability ” framework. Another result is that general propositional formulae cannot be compiled into conditional effects if the plan size should be preserved linearly. This implies that allowing general propositional formulae in preconditions and effect conditions adds another level of difficulty in generating a plan.

We present an extension of language A-Prolog by consistency-restoring rules with preferences, give the semantics of the new language, CR-Prolog, and show how the language can be used to formalize various types of commonsense knowledge and reasoning.

The paper addresses the problem of computing goal orderings, which is one of the longstanding issues in AI planning. It makes two new contributions. First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. Both orderings are defined for simple STRIPS operators as well as for more complex ADL operators supporting negation and conditional effects. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals. Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals. This ...

A rule-based language is proposed for product configuration applications. It is equipped with a declarative semantics providing formal definitions for main concepts in product configuration, including configuration models, requirements and valid configurations. The semantics uses Horn clause derivability to guarantee that each element in a configuration has a justification. This leads to favorable computational properties. For example, the validity of a configuration can be decided in linear time and other computational tasks remain in NP. It is shown that CSP and dynamic CSP can be embedded in the proposed language which seems to be more suitable for representing configuration knowledge. The rule language is closely related to normal logic programs with the stable model semantics. This connection is exploited in the first implementation which is based on a translator from rules to normal programs and on an existing high performance implementation of the stable model semantics, the Smodels system.

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 (...

This is a discussion of some of the achievements and challenges related to representing actions and the design of planners from the perspective of logic programming. We talk about recent work on action languages and translating them into logic programming, on representing possible histories of an action domain by answer sets, on efficient implementations of the answer set semantics and their use for generating plans, and on causal logic and its relation to planning algorithms. Recent progress in these areas may lead to the creation of planners which are based on the ideas of logic programming and combine the use of expressive action description languages with efficient computational procedures. 1 Introduction This is a discussion of some of the achievements and challenges related to representing actions and the design of planners from the perspective of logic programming. Describing the effects of actions and generating plans have been at the center of Artificial Intellige...

The initiative in STRIPS planning has recently been taken by work on propositional satisfiability. Best current planners, like Graphplan, and earlier planners originating in the partial-order or refinement planning community have proved in many cases to be inferior to general-purpose satisfiability algorithms in solving planning problems. However, no explanation of the success of programs like Walksat or relsat in planning has been offered. In this paper we discuss a simple planning algorithm that reconstructs the planner in the background of the SAT/CSP approach.

This paper continues the line of research on representing actions, on the automation of commonsense reasoning and on planning that deals with causal theories and with action language C. We show here that many of the ideas developed in that work can be formulated in terms of logic programs under the answer set semantics, without mentioning causal theories. The translations from C into logic programming that we investigate serve as a basis for the use of systems for computing answer sets to reason about action domains described in C and to generate plans in such domains.

The Smodels system implements the stable model semantics for normal logic programs. It handles a subclass of programs which contain no function symbols and are domain-restricted but supports extensions including built-in functions as well as cardinality and weight constraints. On top of this core engine more involved systems can be built. As an example, we have implemented total and partial stable model computation for disjunctive logic programs. An interesting application method is based on answer set programming, i.e., encoding an application problem as a set of rules so that its solutions are captured by the stable models of the rules. Smodels has been applied to a number of areas including planning, model checking, reachability analysis, product configuration, dynamic constraint satisfaction, and feature interaction. General Information The Smodels system is written in C++ and the source code, test cases and documentation are available at