Authors: Rodolfo Valiente, Praveen K. Pilly
Abstract: Metacognition–the awareness and regulation of one’s cognitive processes–is
central to human adaptability in unknown situations. In contrast, current
autonomous agents often struggle in novel environments due to their limited
capacity for adaptation. We hypothesize that metacognition is a critical
missing ingredient in adaptive autonomous systems, equipping them with the
cognitive flexibility needed to tackle unfamiliar challenges. Given the broad
scope of metacognitive abilities, we focus on two key aspects: competence
awareness and strategy selection for novel tasks. To this end, we propose the
Metacognition for Unknown Situations and Environments (MUSE) framework, which
integrates metacognitive processes–specifically self-awareness and
self-regulation–into autonomous agents. We present two initial implementations
of MUSE: one based on world modeling and another leveraging large language
models (LLMs), both instantiating the metacognitive cycle. Our system
continuously learns to assess its competence on a given task and uses this
self-awareness to guide iterative cycles of strategy selection. MUSE agents
show significant improvements in self-awareness and self-regulation, enabling
them to solve novel, out-of-distribution tasks more effectively compared to
Dreamer-v3-based reinforcement learning and purely prompt-based LLM agent
approaches. This work highlights the promise of approaches inspired by
cognitive and neural systems in enabling autonomous systems to adapt to new
environments, overcoming the limitations of current methods that rely heavily
on extensive training data.
Source: http://arxiv.org/abs/2411.13537v1
