Showing papers by "Hector J. Levesque published in 1999"

An Incremental Interpreter for High-Level Programs with Sensing

Abstract: Like classical planning, the execution of high-level agent programs requires a reasoner to look all the way to a final goal state before even a single action can be taken in the world. This deferral is a serious problem in practice for large programs. Furthermore, the problem is compounded in the presence of sensing actions which provide necessary information, but only after they are executed in the world. To deal with this, we propose (characterize formally in the situation calculus, and implement in Prolog) a new incremental way of interpreting such high-level programs and a new high-level language construct, which together allow much more control to be exercised over when actions can be executed. We argue that such a scheme leads to a practical way to deal with large agent programs containing both nondeterminism and sensing.

Projection Using Regression and Sensors

Abstract: In this paper, we consider the projection task (determining what does or does not hold after performing a sequence of actions) in a general setting where a solution to the frame problem may or may not be available, and where online information from sensors may or may not be applicable. We formally characterize the projection task for actions theories of this sort, and show how a generalized form of regression produces correct answers whenever it can be used. We characterize conditions on action theories, sequences of actions, and sensing information that are sufficient to guarantee that regression can be used, and present a provably correct regressionbased procedure in Prolog for performing the task under these conditions.

A Situation Calculus Approach to Modeling and Programming Agents

Abstract: The notion of computational agents has become very fashionable lately [24, 32] Building such agents seems to be a good way of congenially providing services to users in networked computer systems Typical applications are information retrieval over the internet, automation of common user activities, smart user interfaces, integration of heterogenous software tools, intelligent robotics, business and industrial process modeling, etc The term “agent” is used in many different ways, so let us try to clarify what we mean by it We take an agent to be any active entity whose behavior is usefully described through mental notions such as knowledge, goals, abilities, commitments, etc (This is pretty much the standard usage in artificial intelligence, in contrast to the common view of agents as scripts that can execute on remote machines) Moreover, we will focus on the approach to building applications that involves designing a system as a collection of interacting agents

Query Evaluation and Progression in AOL Knowledge Bases

Abstract: Recently Lakemeyer and Levesque proposed the logic AOC, which amalgamates both the situation calculus and Levesques logic of only knowing. While very expressive the practical relevance of the formalism is unclear because it heavily relies on second-order logic. In this paper we demonstrate that the picture is not as bleak as it may seem. In particular, we show that for large classes of AOL knowledge bases and queries, including epistemic ones, query evaluation requires first-order reasoning only. We also provide a simple semantic definition of progressing a knowledge base. For a particular class of knowledge bases, adapted from earlier results by Lin and Reiter, we show that progression is first-order representable and easy to compute.