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Tatiana V. Guy

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3 papers

1 author row

EUMAS Conference 2018 Conference Paper

Affective Decision-Making in Ultimatum Game: Responder Case

Jitka Homolová
Anastasija Cernecka
Tatiana V. Guy
Miroslav Kárný

Abstract The paper focuses on prescriptive affective decision making in Ultimatum Game (UG). It describes preliminary results on incorporating emotional aspects into normative decision making. One of the players (responder) is modelled via Markov decision process. The responder’s reward function is the weighted combination of two components: economic and emotional. The first component represents pure monetary profit while the second one reflects overall emotional state of the responder. The proposed model is tested on simulated data.

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EUMAS Conference 2017 Conference Paper

Lazy Fully Probabilistic Design: Application Potential

Tatiana V. Guy
Siavash Fakhimi Derakhshan
Jakub Stech

Abstract The article addresses a lazy learning approach to fully probabilistic decision making when a decision maker (human or artificial) uses incomplete knowledge of environment and faces high computational limitations. The resulting lazy Fully Probabilistic Design (FPD) selects a decision strategy that moves a probabilistic description of the closed decision loop to a pre-specified ideal description. The lazy FPD uses currently observed data to find past closed-loop similar to the actual ideal model. The optimal decision rule of the closest model is then used in the current step. The effectiveness and capability of the proposed approach are manifested through example.

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EUMAS Conference 2017 Conference Paper

On Decentralized Implicit Negotiation in Modified Ultimatum Game

Jitka Homolová
Eliska Zugarová
Miroslav Kárný
Tatiana V. Guy

Abstract Cooperation and negotiation are important elements of human interaction within extensive, flatly organized, mixed human-machine societies. Any sophisticated artificial intelligence cannot be complete without them. Multi-agent system with dynamic locally independent agents, that interact in a distributed way is inevitable in majority of modern applications. Here we consider a modified Ultimatum game (UG) for studying negotiation and cooperation aspects of decision making. The manuscript proposes agent’s optimizing policy using Markov decision process (MDP) framework, which covers implicit negotiation (in contrast with explicit schemes as in [ 5 ]). The proposed solution replaces the classical game-theoretical design of agents’ policies by an adaptive MDP that is: (i) more realistic with respect to the knowledge available to individual players; (ii) provides a first step towards solving negotiation essential in conflict situations.

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