Authors:
Anna T. Lawniczak
and
Fei Yu
Affiliation:
University of Guelph, Canada
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
the 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)