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 ...

In this paper we present an extension of logic programming (LP) that is suitable not only for the "rational " component of a single agent but also for the "reactive " component and that can encompass multi-agent systems. We modify an earlier abductive proof procedure and embed it within an agent cycle. The proof procedure incorporates abduction, definitions and integrity constraints within a dynamic environment, where changes can be observed as inputs. The definitions allow rational planning behaviour and the integrity constraints allow reactive, condition-action type behaviour. The agent cycle provides a resource-bounded mechanism that allows the agent's thinking to be interrupted for the agent to record and assimilate observations as input and execute actions as output, before resuming further thinking. We argue that these extensions of LP, accommodating multi-theories embedded in a shared environment, provide the necessary multi-agent functionality. We argue also that our work extends Shoham's Agent0 and the BDI architecture.

In this paper we analyse the differences between rational and reactive agent architectures, and propose a uniform agent architecture that aims to capture both as special cases. For this purpose we employ a proof procedure, to control the agent's behaviour, which combines definitions with integrity constraints. The proof procedure is general, and has been shown elsewhere to unify abductive logic programming, constraint logic programming and semantic query optimisation. We also employ a resource-bounded formalisation of the proof procedure which allows the agent's reasoning to be interrupted and resumed, so that observations and actions can be performed. 1 Introduction The traditional notion of a rational agent in Artificial Intelligence focuses on the agent's thinking process and downplays or ignores its interaction with the environment. This notion has been challenged in recent years by the contrary notion of a reactive agent that focuses on the agent's timely interaction with the env...

. This paper proposes a logic-based framework in which a robot constructs a model of the world through an abductive process whereby sensor data is explained by hypothesising the existence, locations, and shapes of objects. Symbols appearing in the resulting explanations acquire meaning through the theory, and yet are grounded by the robot's interaction with the world. The proposed framework draws on existing logic-based formalisms for representing action, continuous change, space, and shape. INTRODUCTION Without ignoring the lessons of the past, the nascent area of Cognitive Robotics [Lespérance, et al., 1994] seeks to reinstate the ideals of the Shakey project, namely the construction of robots whose architecture is based on the idea of representing the world by sentences of formal logic and reasoning about it by manipulating those sentences. The chief benefits of this approach are, . that it facilitates the endowment of a robot with the capacity to perform high-level reasoning tasks...

In 1969 Cordell Green presented his seminal description of planning as theorem proving with the situation calculus. The most pleasing feature of Green's account was the negligible gap between high-level logical specification and practical implementation. This paper attempts to reinstate the ideal of planning via theorem proving in a modern guise. In particular, the paper shows that if we adopt the event calculus as our logical formalism and employ abductive logic programming as our theorem proving technique, then the computation performed mirrors closely that of a hand-coded partial-order planning algorithm. Soundness and completeness results for this logic programming implementation are given. Finally the paper shows that, if we extend the event calculus in a natural way to accommodate compound actions, then using the same abductive theorem proving techniques we can obtain a hierarchical planner.

In 1969 Cordell Green presented his seminal description of planning as theorem proving with the situation calculus. The most pleasing feature of Green's account was the negligible gap between high-level logical specification and practical implementation. This paper attempts to reinstate the ideal of planning via theorem proving in a modern guise. In particular, I will show that if we adopt the event calculus as our logical formalism and employ abductive logic programming as our theorem proving technique, then the computation performed mirrors closely that of a hand-coded partial order planning algorithm. Furthermore, if we extend the event calculus in a natural way to accommodate compound actions, then using exactly the same abductive theorem prover we obtain a hierarchical planner. All this is a striking vindication of Kowalski's slogan "Algorithm = Logic + Control". Introduction In 1969, Green offered a logical characterisation of planning couched in terms of the situation calculus...

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Any model of the world a robot constructs on the basis of its sensor data is necessarily both incomplete, due to the robot's limited window on the world, and uncertain, due to sensor and motor noise. This paper supplies a logical account of sensor data assimilation in which such models are constructed through an abductive process which hypothesises the existence, locations, and shapes of objects. Noise is treated as a kind of non-determinism, and is dealt with by a consistency-based form of abduction. Introduction The aim of Cognitive Robotics is to design and build mobile robots based on the idea of logical representation [Lespérance, et al., 1994]. By reinstating the ideals of the Shakey project [Nilsson, 1984], Cognitive Robotics has reinvigorated a research programme that has been largely dormant for the past twenty years. This has been made possible by recent advances in the field of common sense reasoning: formalisms now exist for reasoning about action which incorporate robust ...

. This paper presents a programmable logic-based agent control system that interleaves planning, plan execution and perception. In this system, a program is a collection of logical formulae describing the agent's relationship to its environment. Two such programs for a mobile robot are described --- one for navigation and one for map building --- that share much of their code. The map building program incorporates a rudimentary approach to the formalisation of epistemic fluents, knowledge goals, and knowledge producing actions. Introduction Contemporary work in cognitive robotics has demonstrated the viability of logicbased high-level robot control [Lesprance, et al., 1994], [De Giacomo, et al., 1997], [Baral & Tran, 1998], [Shanahan, 2000b]. Building on the progress reported in [Shanahan, 2000b], this paper describes an implemented logic-based, high-level robot control system in the cognitive robotics style. The controller is programmed directly in logic, specifically in the e...

This paper describes the logical foundations of an implemented system which employs resolution-based theorem proving techniques for high-level robot control. The paper offers complementary logical characterisations of perception and planning, and shows how sensing, planning and acting are interleaved to control a real robot. 1 Introduction In the late Sixties, when the Shakey project started [Nilsson, 1984], the vision of robot design based on logical representation seemed both attractive and attainable. Through the Seventies and early Eighties, however, the desire to build working robots led researchers away from logic to more practical but ad hoc approaches to representation. This movement away from logical representation reached its apogee in the late Eighties and early Nineties when Brooks jettisoned the whole idea of representation, along with the so-called sense-model-planact architecture epitomised by Shakey [Brooks, 1991]. However, the Shakey style of architecture, having an ...