Study about Intelligent Virtual Agent: Learning How to Make Compromises

The goal of the study is to provide the learning agent more control over the path to achieving a goal. The study’s precise goals are to investigate the prospect of intelligent virtual agents learning to make acceptable choices only when all user-defined prerequisites for how to attain a goal are met. To fulfil these goals, this chapter presents a modification of the Q-learning algorithm. It is hoped that by doing so, it will be able to achieve aims such as modelling shopping therapy, knowing others’ preferences, determining whether a shopping habit is becoming a problem, recognising cognitive memory problems, and modelling behaviour specific to different age groups. The use of a measurements model (a model of environmental criteria and/or emotional models), represented as a new memory matrix, is introduced to help the Q-learning agent identify the best approach to the objective by meeting specific difficult criteria. The learning agent can compromise a criterion if the goal cannot be achieved by following the pre-set criteria. To achieve the goal, the agent makes the fewest possible choices and suitable concessions. If the criteria are grouped according to their importance, the agent will be able to choose a greater number of acceptable compromises rather than unacceptable ones. Three separate intelligent learning agents were trained using the updated algorithm: a shopping cart agent, a gift-shopping agent, and a broker. Their behaviour has improved as a result of the exams.

Author(S) Details

Dilyana Budakova
Technical University of Sofia, Plovdiv Branch, Bulgaria.

Veselka Petrova-Dimitrova
Technical University of Sofia, Plovdiv Branch, Bulgaria.

Lyudmil Dakovski
European Polytechnical University, Pernik, Bulgaria.

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