Name
Abstract | ||
|---|---|---|
Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task. In a competitive scenario, the agent does not only have a dynamic environment but also is directly affected by the opponents' actions. Observing the Q-values of the agent is usually a way of explaining its behavior, however, it does not show the temporal-relation between the selected actions. We address this problem by proposing the Moody framework that creates an intrinsic representation for each agent based on the Pleasure/Arousal model. We evaluate our model by performing a series of experiments using the competitive multiplayer Chef's Hat card game and discuss how by observing the intrinsic state generated by our model allows us to obtain a holistic representation of the competitive dynamics within the game. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICDL-EpiRob48136.2020.9278125 | 2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) |
Keywords | DocType | ISSN |
Explainable Artificial Intelligence,Reinforcement Learning,Intrinsic Confidence | Conference | 2161-9484 |
ISBN | Citations | PageRank |
978-1-7281-7320-7 | 1 | 0.36 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo V. A. Barros | 1 | 119 | 22.02 |
| Ana Tanevska | 2 | 1 | 0.36 |
| Francisco Cruz | 3 | 2 | 1.06 |
| Alessandra Sciutti | 4 | 62 | 20.57 |