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 ...

Most of the work conducted so far in the eld of logic programming has focused on representing static knowledge, i.e. knowledge that does not evolve with time. To overcome this limitation, in a recent paper, the authors introduced dynamic logic programming. There, they studied and dened the declarative and operational semantics of sequences of logic programs (or dynamic logic programs). Each program in the sequence contains knowledge about some given state, where dierent states may, for example, represent dierent time periods or dierent sets of priorities. But how, in concrete situations, is a sequence of logic programs built? For instance, in the domain of actions, what are the appropriate sequences of programs that represent the performed actions and their eects? Whereas dynamic logic programming provides a way for, given the sequence, determining what should follow, it does not provide a good practical language for the specication of the sequence of updates which may be condi...

For many commonsense reasoning tasks associated with action domains, only a relatively simple kind of causal knowledge is required - knowledge of the conditions under which facts are caused. This note introduces a modal nonmonotonic logic for representing causal knowledge of this kind, relates it to other nonmonotonic formalisms, and shows that a variety of causal theories of action can be expressed in it, including the recently proposed causal action theories of Lin. The new logic extends the causal theories formalism of McCain and Turner, and provides a more adequate semantic account of it. A useful subset of the logic has a concise translation into classical propositional logic, and so can be used for automated planning and reasoning about action. A larger subset is closely related to logic programming under the answer set semantics, yielding another approach to automated reasoning.

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...

We describe a system for specifying the e#ects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the e#ects of actions using domain rules, which are state constraints that can entail new action e#ects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed.

In this paper we argue for a weak form of causality in terms of a dependence relation involving actions, atoms and formulae in order to deal with the frame and ramification problems. This relation allows the atoms to change their value without forcing or causing it. Once integrated in the framework of the Logic of Actions and Plans LAP, it gives us a simple and powerful formalism to reasoning about actions and a decision procedure in terms of tableau methods. We also show how to deal with scenarios involving indeterminate and indirect effects of actions which no other causal framework can handle.

This paper studies the introduction of causality into a logical representation as an embodied ontological feature per se. Technically, this purpose is achieved by introducing the concept of pertinence as an extra valuation of atoms, parallel to the classical truth valuation. Based on this idea, we propose a narrative-based actions formalism which captures at once several aspects of reasoning about change, providing a simple and elegant solution to the frame and the rami cation problems. Finally, we compare this approach to other recent proposals for causal action theories.

The Fluent Calculus is presented as a comprehensive specification and programming language for endowing robots with the ability of task planning in complex environments. Based on a solution to the classical Frame Problem in pure first-order logic, our calculus allows to solve planning problems where the robot has incomplete state knowledge and which involve the use of sensors, actions with uncertain effects, actions with ramications (i.e., indirect effects), and the concurrent execution of actions. Our new theory of sensing is distinguished by its simple inference scheme for calculating the effects of actions on state knowledge and by its comparatively simple account of non-knowledge. A realization of the Fluent Calculus by means of constraint logic programming is presented. Outstanding novel features of the system are to solve the inferential Frame Problem under incomplete state information, to allow for solving planning problems with knowledge goals, and to combine nondeterminism, concurrency, and ramification.

We discuss a new approach to deal with causality in reasoning about actions and change. First, we introduce the logic of pertinence, L to describe knowledge over a static domain|especially the causal inuences.

In [ALP + 00] we proposed a comprehensive solution to the problem of knowledge base updates. Given the original knowledge base KB and a set of update rules represented by the updating knowledge base KB 0 , we defined a new updated knowledge base KB = KB KB 0 that constitutes the update of the knowledge base KB by the knowledge base KB 0 : In [APPP99] we introduced a fully declarative, high-level language for knowledge updates called LUPS that describes transitions between consecutive knowledge states and can therefore be viewed as a language for dynamic knowledge representation. This paper has two main objectives. One is to show that the dynamic knowledge representation paradigm introduced in [ALP + 00] and the associated language LUPS, de ned in [APPP99], constitute natural, powerful and expressive tools for representing dynamically changing knowledge. We do so by demonstrating the applicability of the dynamic knowledge representation paradigm and the langu...