Continuous Planning for Virtual Environments

Continuous Planning for Virtual Environments

Nikos Avradinis (University of Piraeus, Greece and University of Salford, UKUniveresity of Piraeus, Greece) and Themis Panayiotopoulos (University of Salford, UK)
Copyright: © 2005 |Pages: 32
DOI: 10.4018/978-1-59140-450-7.ch005
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Abstract

This chapter discusses the application of intelligent planning techniques to virtual agent environments as a mechanism to control and generate plausible virtual agent behaviour. The authors argue that the real world-like nature of intelligent virtual environments (IVEs) presents issues that cannot be tackled with a classic, off-line planner where planning takes place beforehand and execution is performed later, based on a set of precompiled instructions. What IVEs call for is continuous planning, a generative system that will work in parallel with execution, constantly re-evaluating world knowledge and adjusting plans according to new data. The authors argue further on the importance of incorporating the modelling of the agents’ physical, mental and emotional states as an inherent feature in a continuous planning system targeted towards IVEs, necessary to achieve plausibility in the produced plans and, consequently, in agent behaviour.

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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