A meta-cognitive architecture for planning in uncertain environments

Abstract

The behavior of an artificial agent performing in a natural environment is influenced by many different pressures and needs coming from both external world and internal factors, which sometimes drive the agent to reach conflicting goals. At the same time, the interaction between an artificial agent and the environment is deeply affected by uncertainty due to the imprecision in the description of the world, and the unpredictability of the effects of the agent’s actions. Such an agent needs meta-cognition in terms of both self-awareness and control. Self-awareness is related to the internal conditions that may possibly influence the completion of the task, while control is oriented to performing actions that maintain the internal model of the world and the perceptions aligned. In this work, a general meta-cognitive architecture is presented, which is aimed at overcoming these problems. The proposed architecture describes an artificial agent, which is capable to combine cognition and meta-cognition to solve problems in an uncertain world, while reconciling opposing requirements and goals. While executing a plan, such an agent reflects upon its actions and how they can be affected by its internal conditions, and starts a new goal setting process to cope with unforeseen changes. The work defines the concept of “believability” as a generic uncertain quantity, the operators to manage believability, and provides the reader with the u-MDP that is a novel MDP able to deal with uncertain quantities expressed as possibility, probability, and fuzziness. A couple u-MDPs are used to implement the agent’s cognitive and meta-cognitive module. The last one is used to perceive both the physical resources of the agent’s embodiment and the actions performed by the cognitive module in order to issue goal setting and re-planning actions.