Comparing Multiple Models of Reasoning: An Agent-based Approach

Meulen, A. van der (2016) Comparing Multiple Models of Reasoning: An Agent-based Approach. Master's Thesis / Essay, Artificial Intelligence.

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Abstract

Being able to negotiate under pressure is generally considered to be a useful skill that one can apply to maximize one's gains in a negotiation situation. In this study, we have looked at negotiation situations using a game called Coloured Trails. Coloured Trails is a decision-making board game in which human or agent players are given a finite set of chips they need to hand in to cross corresponding fields on the game board. The players have to reach or get close to a goal position on this board. They are usually unable to reach this goal position with their initial set of chips, and needs to trade with a purely responsive agent to better their position. We consider a version of Coloured Trails in which a human competes with an agent for the opportunity to trade with a responding agent in order to obtain chips that will bring them closer to their goal. We have looked at the effects of different models on the learning behaviour of both participants and agents. To achieve this, we have implemented two models: a model that uses a belief-adjustment theory of mind system and a model that uses parameter-based input. We have implemented two variations of both of these models with Java agents. After this, participants were asked to play 10 one-shot games of Coloured Trails against each of the four agents, after which the participant performance was evaluated. In the end, we found that while it is hard to highlight differences in the participants' performance across the models, participants in the study showed that they were clearly improving their position and speed throughout the experiment. This shows that Coloured Trails may be an efficient game in teaching players to balance their own goals versus those of potential competitors and negotiation partners. This in turn teaches people to negotiate more efficiently. In the future, it may be beneficial to increase the contrast between games played against different agents, in order to accurately highlight the differences of the respective models in terms of learning.

Item Type:	Thesis (Master's Thesis / Essay)
Degree programme:	Artificial Intelligence
Thesis type:	Master's Thesis / Essay
Language:	English
Date Deposited:	15 Feb 2018 08:25
Last Modified:	15 Feb 2018 08:25
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/14577

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