Learning from Failures for Cognitive Flexibility

Cognitive flexibility is an important goal in the computational modeling of higher cognition. An agent operating in the world that changes over time should adapt to the changes and update its knowledge according to them. In this paper, we report our progress on implementing a constraint-based mechanism for learning from failures in a cognitive architecture, ICARUS. We review relevant features of the architecture, and describe the learning mechanism in detail. We also discuss the challenges encountered during the implementation and describe how we solved them. We then provide some experimental observations and conclude after a discussion on related and future work.