Name
Title | ||
|---|---|---|
Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture |
Abstract | ||
|---|---|---|
We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1111/tops.12573 | TOPICS IN COGNITIVE SCIENCE |
Keywords | DocType | Volume |
Explainable artificial intelligence, Cognitive modeling, Common ground, Salience, Instance-based learning, Deep reinforcement learning | Journal | 14 |
Issue | ISSN | Citations |
4 | 1756-8757 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Mitsopoulos | 1 | 0 | 0.34 |
| Sterling Somers | 2 | 0 | 0.34 |
| Joel Schooler | 3 | 0 | 0.34 |
| Christian Lebiere | 4 | 1152 | 253.98 |
| Peter Pirolli | 5 | 3661 | 538.83 |
| Robert Thomson | 6 | 0 | 0.34 |