Credit Assignment: Challenges and Opportunities in Developing Human-like
AI Agents
- URL: http://arxiv.org/abs/2307.08171v1
- Date: Sun, 16 Jul 2023 23:11:26 GMT
- Title: Credit Assignment: Challenges and Opportunities in Developing Human-like
AI Agents
- Authors: Thuy Ngoc Nguyen and Chase McDonald and Cleotilde Gonzalez
- Abstract summary: Temporal credit assignment is crucial for learning and skill development in natural and artificial intelligence.
We use a cognitive model based on a theory of decisions from experience to test different credit assignment mechanisms in a goal-seeking navigation task.
We found that an IBL model that gives equal credit assignment to all decisions is able to match human performance better than other models.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Temporal credit assignment is crucial for learning and skill development in
natural and artificial intelligence. While computational methods like the TD
approach in reinforcement learning have been proposed, it's unclear if they
accurately represent how humans handle feedback delays. Cognitive models intend
to represent the mental steps by which humans solve problems and perform a
number of tasks, but limited research in cognitive science has addressed the
credit assignment problem in humans and cognitive models. Our research uses a
cognitive model based on a theory of decisions from experience, Instance-Based
Learning Theory (IBLT), to test different credit assignment mechanisms in a
goal-seeking navigation task with varying levels of decision complexity.
Instance-Based Learning (IBL) models simulate the process of making sequential
choices with different credit assignment mechanisms, including a new IBL-TD
model that combines the IBL decision mechanism with the TD approach. We found
that (1) An IBL model that gives equal credit assignment to all decisions is
able to match human performance better than other models, including IBL-TD and
Q-learning; (2) IBL-TD and Q-learning models underperform compared to humans
initially, but eventually, they outperform humans; (3) humans are influenced by
decision complexity, while models are not. Our study provides insights into the
challenges of capturing human behavior and the potential opportunities to use
these models in future AI systems to support human activities.
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