The following publications/drafts are available as PostScript/PDF files. The
HTML sections are mostly more detailed, but do
not cover all papers.
Artificial Intelligence in Computer Games - State of the Art and Future Directions.
ACM Queue 1(10), 58-65, 2004.
An overview article about the AI techniques used in today's computer games.
Intelligent Agents for Computer Games.
In Marsland, T. A., and Frank, I. (eds.), Computers and Games, Second International Conference, CG 2000, Springer LNCS 2063, 414-422, 2002.
This article describes the different types of autonomous agents that can be used
for computer games and gives a very brief introduction on the
Beyond the Plan-Length Criterion.
In Nareyek, A. (ed.), Local Search for Planning and Scheduling,
Springer LNAI 2148, 55-78, 2001.
This paper presents the general approach of the
EXCALIBUR agent's planning system and
gives an overview of the applied techniques. Learn about the Orc Quest and
how to double a wizard!
Constraint-Based Agents - An Architecture for Constraint-Based Modeling and Local-Search-Based Reasoning for Planning and Scheduling in Open and Dynamic Worlds.
Reading, Springer LNAI 2062, 2001.
This book describes the state of the EXCALIBUR project as of March 2001.
The Agent Model
Open World Planning as SCSP.
In Papers from the AAAI-2000 Workshop on Constraints and AI Planning, Technical
Report, WS-00-02, 35-46. AAAI Press, Menlo Park, California, 2000.
The planning model for the EXCALIBUR agents
is given here. For the handling of further aspects of incomplete knowledge,
please have a look at the publication "A Planning Model for Agents in Dynamic
and Uncertain Real-Time Environments" below.
A Planning Model for Agents in Dynamic and Uncertain Real-Time Environments.
In Proceedings of the 1998 AIPS Workshop on Integrating Planning, Scheduling and
Execution in Dynamic and Uncertain Environments, Technical Report, WS-98-02,
7-14. AAAI Press, Menlo Park, California, 1998.
This document describes the basic planning model for
EXCALIBUR's agents. The formal model
(slightly revised) is described in the publication "Open World Planning as
SCSP" (see above).
Planning to Plan - Integrating Control Flow.
In Proceedings of the International Workshop on Heuristics (IWH'02), 79-84,
2002. Also appeared in Tsinghua Science and Technology 8(1): 1-7, 2003.
The publication describes how an agent can reason about its own reasoning
process. This is necessary for tasks like negotiation or for explicitly
reasoning about ways to optimize its planning process.
Planning in Dynamic Worlds: More Than External Events.
In Proceedings of the IJCAI-03 Workshop on Agents and Automated Reasoning, 30-35.
An agent must not only consider the impact of his own actions but also how the world around him evolves. The publications describes how we approach this problem for our agent architecture.
An Agent's Reasoning
Using Global Constraints for Local Search.
In Freuder, E. C., and Wallace, R. J. (eds.), Constraint Programming and
Large Scale Discrete Optimization, American Mathematical Society
Publications, DIMACS Volume 57, 9-28, 2001.
This document describes our base technology to allow for anytime properties
and the integration of domain-dependent knowledge.
Local-Search Heuristics for Generative Planning.
In Proceedings of the Fifteenth Workshop on AI in Planning, Scheduling,
Configuration and Design (PuK 2001), 56-70, 2001.
This paper presents the local-search heuristics applied in the
EXCALIBUR agent's planning system
and demonstrates an application to example problems.
Choosing Search Heuristics by Non-Stationary Reinforcement Learning.
In Resende, M. G. C., and de Sousa, J. P. (eds.), Metaheuristics: Computer Decision-Making, Kluwer Academic Publishers, 523-544, 2003.
The article compares different selection schemes to choose from a number of
alternatives. These strategies are applied in the
EXCALIBUR agent's planning system for
A Modular Graphical User Interface for Interactive Planning.
Diploma Thesis, Technical University of Munich, Department of Computer Science, 2004.
The thesis describes the development and functionality of the graphical planning interface that we use to visualize (and debug) the planning process.
Constraints and AI Planning.
IEEE Intelligent Systems, 20(2), 62-72, 2005.
A survey on constraint-based approaches that can be used for an agent's action planning. Our own approach, based on so-called structural constraint satisfaction (see below for more details), is briefly described as well.
Structural Constraint Satisfaction.
In Papers from the 1999 AAAI Workshop on Configuration, Technical Report,
WS-99-05, 76-82. AAAI Press, Menlo Park, California, 1999.
This document introduces the concept of structural constraint satisfaction,
which is necessary to handle a variable plan structure. However, it is based
on refinement search. The paper "Applying Local Search to Structural Constraint
Satisfaction" (see below) extends this concept to
local search approach.
Applying Local Search to Structural Constraint Satisfaction.
In Proceedings of the 1999 IJCAI Workshop on Intelligent Workflow and Process
Management: The New Frontier for AI in Business, 1999.
The concept of structural constraint satisfaction is combined with local search
and global constraints. This forms EXCALIBUR's
base technology for an agent's behavior planning.
Realisierung struktureller Constraints.
Diploma Thesis, Technical University of Berlin, Department of Electrical Engineering and Computer Science, 2003.
The thesis explains how the handling of structural constraints was realized for our engine. Because of the complexity of this reasoning, however, we will most likely use this feature only for internal testing/prototyping. For the externally available planning process, we will ensure that heuristics do not make changes that lead to inconsistent graphs. Anyone who is interested in using the feature of structural constraints (e.g., when extending or changing our planning model) can activate it in a very simple way.