Anthropomorphic Self-Models for Metareasoning Agents

Representations of an AI agent’s mental states and processes are necessary to enable metareasoning, i.e. thinking about thinking. However, the formulation of suitable representations remains an outstanding AI research challenge, with no clear consensus on how to proceed. This paper outlines an approach involving the formulation of anthropomorphic self-models, where the representations that are used for metareasoning are based on formalizations of commonsense psychology. We describe two research activities that support this approach, the formalization of broad-coverage commonsense psychology theories and use of representations in the monitoring and control of objectlevel reasoning. We focus specifically on metareasoning about memory, but argue that anthropomorphic self-models support the development of integrated, reusable, broadcoverage representations for use in metareasoning systems. Self-models in Metareasoning Cox and Raja (2007) define reasoning as a decision cycle within an action-perception loop between the ground level (doing) and the object level (reasoning). Metareasoning is further defined as a second loop, where this reasoning is itself monitored and controlled in order to improve the quality of the reasoning decisions that are made (Figure 1). Figure 1. Multi-level model of reasoning It has long been recognized (e.g., McCarthy, 1958) that to better understand and act upon the environment, an agent should have an explicit, declarative representation of the states and actions occurring in that environment. Thus the task at the object level is to create a declarative model of the world and to use such a representation to facilitate the selection of actions at the ground level. It follows also that to reason about other agents in the world (e.g., to Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. anticipate what they may do in the future), it helps to have a representation of the agents in the world, what they know, and how they think. Likewise an explicit representation of the self supports reasoning about oneself and hence facilitates metareasoning. Representations provide structure and enable inference. They package together related assertions so that knowledge is organized and brought to bear effectively and efficiently. One of the central concerns in the model of metareasoning as shown in Figure 1 is the character of the information that is passed between the object level and the meta-level reasoning modules to enable monitoring and control. Cast as a representation problem, the question becomes: How should an agent’s own reasoning be represented to itself as it monitors and controls this reasoning? Cox and Raja (2007) describe these representations as models of self, which serve to control an agent’s reasoning choices, represent the product of monitoring, and coordinate the self in social contexts. Self-models have been periodically explored in previous AI research since Minsky (1968), and explicit self-models have been articulated for a diverse set of reasoning processes that include threat detection (Birnbaum et al., 1990), case retrieval (Fox & Leake, 1995), and expectation management (Cox, 1997). Typically built to demonstrate a limited metareasoning capacity, these self-models have lacked several qualities that should be sought in future research in this area, including: 1. Broad coverage: Self-models should allow an agent to reason about and control the full breadth of their object-level reasoning processes. 2. Integrated: Self-models of different reasoning processes should be compatible with one another, allowing an agent to reason about and control the interaction between different reasoning subsystems. 3. Reusable: The formulation of self-models across different agents and agent architectures should have some commonalities that allow developers to apply previous research findings when building new systems. Despite continuing interest in metareasoning over the last two decades (see Anderson & Oates, 2007; Cox, 2005), there has been only modest progress toward the development of self-models that achieve these desirable qualities. We speculate that this is due, in part, to an