Werewolf Game in MAS

Werewolf Game in MAS

Failed to execute the [velocity] macro. Cause: [The execution of the [velocity] script macro is not allowed in [xwiki:Courses.Environment]. Check the rights of its last author or the parameters if it's rendered from another script.]. Click on this message for details.

Failed to execute the [velocity] macro. Cause: [The execution of the [velocity] script macro is not allowed in [xwiki:Courses.Mas2122.Projects.WerewolfMas]. Check the rights of its last author or the parameters if it's rendered from another script.]. Click on this message for details.

Author

Abstract

The goal of this project is to develop a Multi Agent implementation of the famous game called Werewolf. This game is based on the rivalry between two factions of agents (i.e. farmers and wolves) who challenge each other during the development of each game in order to eliminate as many agents as possible from the opposing faction. This game contains multiple components common to multi-agent systems: there is an advanced communication mechanism associated with each faction, which also contains different privacy policies for each of these. Furthermore, each agent is independent and simultaneously implements both the principle of cooperation with respect to the agents of his faction and the principle of competition with respect to the agents of the opposing faction. In addition to this, we have also implemented several elements of this game in the form of ”artifacts”, mainly represented by the ”perks” available to each agent. Finally, each agent has been provided with an ”intelligence” mechanism through the structuring of a Bayesian network capable of representing the probabilities associated with each game event throughout its continuation. The results presented were obtained from the simulation of multiple games based, in turn, on multiple different configurations relating both to the game and to the strategies associated with each agent. The results themselves highlighted the correctness of the completed implementation, highlighted the differences with respect to the possible game policies associated with each agent and, at the same time, brought to light the effects of the artifacts with respect to the game strategy of each agent.

Outcome

Tags:
    

Courses
home  •  search  •  all

Courses / Views
home
clouds
tags  •  project tags
per year
2022/2023  •  2021/2022  •  2020/2021  •  2019/2020  •  2018/2019  •  2017/2018  •  2016/2017  •  2015/2016  •  2014/2015  •  2013/2014–1996/1997

Course

— a.y.

2021/2022

— credits

6

— cycle

2nd cycle

— language

wgb.gif

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

— 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

Partita IVA: 01131710376 — Copyright © 2008–2023 APICe@DISI – PRIVACY