AI Planning and Intelligent Agents

AI Planning and Intelligent Agents

Catherine C. Marinagi (Technological Educational Institution of Kavala, Greece), Themis Panayiotopoulos (University of Piraeus, Greece) and Constantine D. Spyropoulos (Institute of Informatics & Telecommunications NCSR, Greece)
Copyright: © 2005 |Pages: 34
DOI: 10.4018/978-1-59140-450-7.ch007
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Abstract

This chapter provides an overview of complementary research in the active research areas: AI planning technology and intelligent agents technology. It has been widely acknowledged that modern intelligent agents approaches should combine methodologies, techniques and architectures from many areas of computer science, cognitive science, operation research, cybernetics, and so forth. AI planning is an essential function of intelligence that is necessary in intelligent agents applications. This chapter presents the current state-of-the-art in the field of intelligent agents, focusing on the role of AI planning techniques. It sketches a typical classification of agents, agent theories and architectures from an AI planning perspective, it briefly introduces the reader to the basic issues of AI planning, and it presents different AI planning methodologies implemented in intelligent agents applications. The authors aim at stimulating research interest towards the integration of AI planning with intelligent agents.

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Table of Contents
Preface
Ioannis Vlahavas, Dimitris Vrakas
Acknowledgments
Chapter 1
Thomas Eiter, Wolfgang Faber, Gerald Pfeifer, Axel Polleres
This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation &... Sample PDF
Declarative Planning and Knowledge Representation in an Action Language
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Chapter 2
Max Garagnani
This chapter describes a model and an underlying theoretical framework for hybrid planning. Modern planning domain description languages are based... Sample PDF
A Framework for Hybrid and Analogical Planning
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Chapter 3
Dimitris Vrakas, Grigorios Tsoumakas, Nick Bassiliakes, Ioannis Vlahavas
This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their... Sample PDF
Machine Learning for Adaptive Planning
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Chapter 4
José Luis Ambite, Craig A. Knoblock, Steven Minton
Planning by Rewriting (PbR) is a paradigm for efficient high-quality planning that exploits declarative plan rewriting rules and efficient local... Sample PDF
Plan Optimization by Plan Rewriting
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Chapter 5
Nikos Avradinis, Themis Panayiotopoulos
This chapter discusses the application of intelligent planning techniques to virtual agent environments as a mechanism to control and generate... Sample PDF
Continuous Planning for Virtual Environments
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Chapter 6
Jeroen Valk, Mathijs de Weerdt, Cees Witteveen
Multi-agent planning comprises planning in an environment with multiple autonomous actors. Techniques for multi-agent planning differ from... Sample PDF
Coordination in Multi-Agent Planning with an Application in Logistics
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Chapter 7
Catherine C. Marinagi, Themis Panayiotopoulos, Constantine D. Spyropoulos
This chapter provides an overview of complementary research in the active research areas: AI planning technology and intelligent agents technology.... Sample PDF
AI Planning and Intelligent Agents
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Chapter 8
Amedeo Cesta, Simone Fratini, Angelo Oddi
This chapter proposes to model a planning problem (e.g., the control of a satellite system) by identifying a set of relevant components in the... Sample PDF
Planning with Concurrency, Time and Resources: A CSP-Based Approach
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Chapter 9
Martha E. Pollack, Ioannis Tsamardinos
The Simple Temporal Problem (STP) formalism was developed to encode flexible quantitative temporal constraints, and it has been adopted as a... Sample PDF
Efficiently Dispatching Plans Encoded as Simple Temporal Problems
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Chapter 10
Roman Bartak
As the current planning and scheduling technologies are coming together by assuming time and resource constraints in planning or by allowing... Sample PDF
Constraint Satisfaction for Planning and Scheduling
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About the Authors