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Authors:Ching Choo Voon: Singapore Polytechnic, Singapore
Ching Siang Boo: Singapore Polytechnic, Singapore
Danny Hong Tai Neo: Singapore Polytechnic, Singapore
Liang Jie Wong: Singapore Polytechnic, Singapore
Publication title:SP-United: Applying Last Action As Feedback For A.I. Agents
Conference:RobocCup-99 Team Descriptions. Simulation League
Publication type: Abstract and Fulltext
Article No.:028
Abstract:This paper describes the use of a last action feedback loop to compliment the use of a decision tree in a reactive agent. It aims to create a team of reactive agents that is semi - goal orientated.

Computer programs; artificially intelligent or not; will generally display the characteristic of performing identical actions when given near identical situations. This is due to the lack of human intuition to generate the element of unpredictability. The simulator league is also similar. A client in the simulator league no matter how advanced has to make decisions based on its creators source codes. Since the span of the source codes is usually very general it limits the flexibility of the client player. Hence if we could provide identical situations to the client we would almost be sure that it would react identically. This would be true; especially if its last action was successful. Therefore; if we could just remember this last action taken by the enemy and the condition under which it happened we could exploit this weakness and use it to our advantage.

No. of pages:1
Series:Linköping Electronic Conference Proceedings
ISSN (print):1650-3686
ISSN (online):1650-3740
Publisher:Linköping University Electronic Press; Linköpings universitet

Ching Choo Voon, Ching Siang Boo, Danny Hong Tai Neo, Liang Jie Wong (1999). SP-United: Applying Last Action As Feedback For A.I. Agents, RobocCup-99 Team Descriptions. Simulation League;article=028 (accessed 2/9/2016)