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Authors: Anna T. Lawniczak and Fei Yu

Affiliation: University of Guelph, Canada

ISBN: 978-989-758-219-6

Keyword(s): Agents, Cognitive Agents, Autonomous Robots, Cellular Automaton, Decision Making, Learning, Knowledge Base, Computational Intelligence.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Autonomous Systems ; Bioinformatics ; Biomedical Engineering ; Cognitive Robotics ; Collective Intelligence ; Distributed and Mobile Software Systems ; Enterprise Information Systems ; Informatics in Control, Automation and Robotics ; Information Systems Analysis and Specification ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Methodologies and Technologies ; Mobile Agents ; Multi-Agent Systems ; Operational Research ; Robotics and Automation ; Simulation ; Software Engineering ; Symbolic Systems

Abstract: We compare the performance of simple cognitive agents, learning to cross a Cellular Automaton (CA) based highway, for two decision formulas used by the agents’ in their decision-making process. We describe the main features of the simulation model: CA based highway traffic environment, agents and their decision and learning mechanisms. The agents use a type of “observational social learning” strategy, i.e. they observe the performance of other agents and they try to mimic what worked for other agents and they try to avoid what did not work for the other agents. In the decision-making process of deciding whether to cross the highway or to wait, depending on the simulation setup, the agents use one of the two decisions formulas: the first one based only on the assessment of the agents crossing decisions (cDF), or the second one based on the assessment of the agents crossing and waiting decisions (cwDF). Our simulations show that the performance of agents using cwDF is much better than t he performance of the agents using cDF in their decision making process. We measure the agents’ performance by the numbers of agents: who crossed successfully, who were killed and those who are still queuing to cross at simulation end. (More)

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Paper citation in several formats:
Lawniczak, A. and Yu, F. (2017). Comparison of Agents’ Performance in Learning to Cross a Highway for Two Decisions Formulas.In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-219-6, pages 208-219. DOI: 10.5220/0006193102080219

@conference{icaart17,
author={Anna T. Lawniczak. and Fei Yu.},
title={Comparison of Agents’ Performance in Learning to Cross a Highway for Two Decisions Formulas},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2017},
pages={208-219},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006193102080219},
isbn={978-989-758-219-6},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Comparison of Agents’ Performance in Learning to Cross a Highway for Two Decisions Formulas
SN - 978-989-758-219-6
AU - Lawniczak, A.
AU - Yu, F.
PY - 2017
SP - 208
EP - 219
DO - 10.5220/0006193102080219

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