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Adapting an
Agent to a
Similar
Environment
Our Experience
• We tried using an agent architecture that
was successful in one domain in another
domain which, when measured against
against existing taxonomies, was virtually
indistinguishable.
• The transfer was completely unsuccessful
or … How General are Agent Architectures?
Paul Scerri and Nancy Reed
Department of Computer and Information Science
Linköpings Universitet, S-581 83, Linköping, Sweden
pausc, nanre @ida.liu.se
http://www.ida.liu.se/~pausc , ~nanre
The Lesson
• Very small differences between domains
may render an architecture successful in one
domain completely unsuccessful in another
• We need to look at better/other domain
characteristics in order to assess whether a
particular architecture may be applied to a
certain domain
The Architecture
• Layered, behavior-based system
• Winner takes all arbitration at each level
RoboCup
Similar Characteristics
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High autonomy
No Mobility
No Adaptability
Inference Ability
Unpredictable Environment
Real-time
Information Gathering
Limited Communication
Team Plans
High Localization
Inaccessible World
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High Reactivity
Temporal Scale
Knowledge Level
Uncertain Sensors
Historical
Uncontrollable Environment
Hostile World
Resource Management
Coordination Required
Integration
Teleological World
Air-combat Simulation
Critical Difference
• In both domains agents have multiple
simultaneous goals but in RoboCup one of
the simultaneous goals is always obstacle
avoidance
• Hence RoboCup allowed the specialization
of building obstacle avoidance into the low
level skills and using winner-takes-all
arbitration
• Winner-takes-all fails for air-combat!
RoboCup Performance
• Team developed using the architecture
competed in 1998 World RoboCup
championships in Paris
– Reached quarter finals
• A new team will compete in the 1999 World
RoboCup championships in Stockholm
(July/August)
Acknowledgements
This work is supported by:
• Saab AB, Operational Analysis
• The Swedish Government under NUTEK grant #s
IK1P-97-09677 and IK1P-98-06280
• Linköping University under CENIIT grant 98.6
References
• S. Hanks, M. Pollack and P. Cohen. Benchmarks, testbeds, controlled
experimentation, and the design of agent architectures. AI Magazine,
14(4):17-42, 1993.
• H. Nwana. Software Agents: An Overview. Knowledge Engineering
Review, 11(3):1-40, September 1996.
• P. Rosenbloom, J. Laird, A Newell and R McCarl. A preliminary
analysis of SOAR as a basis for general intelligence. Artificial
Intelligence, 47:289-325, 1991.
• M. Huhns and M. Singh. Agents and multiagent systems: Themes
approaches and challenges. In Readings in Agents, pages 1-23, Morgan
Kaufmann, 1997.
• P. Scerri, S. Coradeschi and A. Törne. A user oriented system for
developing behavior based agents. In Proc. of RoboCup ’98, Paris.
1998.