Title :
Naïve creature learns to cross a highway in a simulated CA-like environment
Author :
Lawniczak, Anna T. ; Di Stefano, Bruno N. ; Ernst, Jason B.
Author_Institution :
Univ. of Guelph, Guelph, ON, Canada
Abstract :
We present a model of simple cognitive agents, called “creatures”, and their learning process, a type of “social observational learning”, that is each creature learns from the behaviour of other creatures. The creatures may experience fear and/or desire, and are capable of evaluating if a strategy has been applied successfully and of applying this strategy again with small changes to a similar but new situation. The creatures are born as “tabula rasa”; i.e. without built-in knowledge base of their environment and as they learn they build this knowledge base. We study learning outcomes of a population of such creatures when they are learning how to safely cross various types of highways. The highways are implemented as a modified Nagel-Schreckenberg model, a CA based highway model, and each creature is provided with mechanism to reason to cross safely the highway. We present selected simulation results and their analysis.
Keywords :
cellular automata; learning systems; mobile robots; CA based highway model; autonomous robots; cellular automata; cognitive agents; modified Nagel-Schreckenberg model; naïve creature; simulated CA-like environment; social observational learning; tabula rasa; Knowledge based systems; Road transportation; Robots; Simulation; Solid modeling; Throughput; Vehicles; Nagel-Schreckenberg model; agents; autonomous robots; cellular automata; cognitive agents; computational intelligence; global cellular automata; global cellular automata with write; learning;
Conference_Titel :
Intelligent Agents (IA), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/IA.2014.7009455