This paper proposes a new logic programming language called GOLOG whose interpreter automatically maintains an explicit representation of the dynamic world being modeled, on the basis of user supplied axioms about the preconditions and effects of actions and the initial state of the world. This allows programs to reason about the state of the world and consider the effects of various possible courses of action before committing to a particular behavior. The net effect is that programs may be written at a much higher level of abstraction than is usually possible. The language appears well suited for applications in high level control of robots and industrial processes, intelligent software agents, discrete event simulation, etc. It is based on a formal theory of action specified in an extended version of the situation calculus. A prototype implementation in Prolog has been developed.

this paper, including transaction logs and historical queries, the complexity of query evaluation, actualized transactions, logic programming approaches to updates, database views and state constraints. / This paper consolidates and expands on a variety of results, some of which have been described elsewhere (Reiter [46, 45, 44])

This paper describes a novel approach to high-level agent programming based on a highly developed logical theory of action. The user provides a specification of the agents' basic actions (preconditions and effects) as well as of relevant aspects of the environment, in an extended version of the situation calculus. He can then specify behaviors for the agents in terms of these actions in a programming language where one can refer to conditions in effect in the environment. When an implementation of the basic actions is provided, the programs can be executed in a real environment; otherwise, a simulated execution is still possible. The interpreter automatically maintains the world model required to execute programs based on the specification. The theoretical framework includes a solution to the frame problem, allows agents to have incomplete knowledge of their environment, and handles perceptual actions. The theory can also be used to prove programs correct. A simple meeting sc...

. At a workshop held in Toulouse, France in 1977, Gallaire, Minker and Nicolas stated that logic and databases was a field in its own right (see [131]). This was the first time that this designation was made. The impetus for this started approximately twenty years ago in 1976 when I visited Gallaire and Nicolas in Toulouse, France, which culminated in a workshop held in Toulouse, France in 1977. It is appropriate, then to provide an assessment as to what has been achieved in the twenty years since the field started as a distinct discipline. In this retrospective I shall review developments that have taken place in the field, assess the contributions that have been made, consider the status of implementations of deductive databases and discuss the future of work in this area. 1 Introduction As described in [234], the use of logic and deduction in databases started in the late 1960s. Prominent among the developments was the work by Levien and Maron [202, 203, 199, 200, 201] and Kuhns [1...

. 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, 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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We present an attempt to reconcile the theoretical work on reasoning about action with the realization of agents, in particular mobile robots. Specifically, we present a logical framework for representing dynamic systems based on description logics, which allows for the formalization of sensing actions. We address the generation of conditional plans by defining a suitable reasoning method in which a plan is extracted from a constructive proof of a query expressing a given goal. We also present an implementation of such a logical framework, which has been tested on the mobile robot "Tino".

This paper proposes logic programs as a specification for robot control. These provide a formal specification of what an agent should do depending on what it senses, and its previous sensory inputs and actions. We show how to axiomatise reactive agents, events as an interface between continuous and discrete time, and persistence, as well as axiomatising integration and differentiation over time (in terms of the limit of sums and differences). This specification need not be evaluated as a Prolog program; we use can the fact that it will be evaluated in time to get a more efficient agent. We give a detailed example of a nonholonomic maze travelling robot, where we use the same language to model both the agent and the environment. One of the main motivations for this work is that there is a clean interface between the logic programs here and the model of uncertainty embedded in probabilistic Horn abduction. This is one step towards building a decisiontheoretic planning system where the output of the planner is a plan suitable for actually controlling a robot. 1

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 ...