Tic Tac Toe with AgentSpeak in MAS

classic project

Authors

Abstract

In this project a multi-agent system consisting of two agents competing in tic-tac-toe was implemented. The system was developed in AgentSpeak using Jason. Two agent types were implemented; a random agent playing random moves and a strategic agent using the Mini-max algorithm to calculate it's moves. The strategic agent was tested against both a random agent and against a strategic agent using the same strategy. These results were compared to the work of Govind G Nair, who uses reinforcement learning to make strategic moves. The Mini-max agent performs optimally when playing against an equal opponent. Further, when the opponent is a random agent the reinforcement learning agent achieves a higher winrate than the Mini-max agent. However, the reinforcement learning agent sometimes looses to the random agent, while the Mini-max agent always wins or ties.

Outcome

Course

— a.y.

2021/2022

— credits

6

— cycle

2nd cycle

— language

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Teachers

— professor

Andrea Omicini

— other professors

Roberta Calegari

Context

— university

Alma Mater Studiorum-Università di Bologna

— campus

Bologna

— department / faculty / school

DISI

— 2nd cycle

 9063 Artificial Intelligence 

URLs & IDs

AMS Page
course on Virtuale
virtual room
Course Timetable

— course ID

91267

Related Courses

— components

Multi-Agent Systems (Module 1) (2nd Cycle, 2021/2022) — Andrea Omicini  |  Multi-Agent Systems (Module 2) (2nd Cycle, 2021/2022) — Roberta Calegari

— related

Project Work in Multi-Agent Systems (2nd Cycle, 2021/2022) — Andrea Omicini

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