Abductive Metareasoning for Truth-Seeking Agents

My research seeks to answer the question of how any agent that is tasked with making sense of its world, by finding explanations for evidence (e.g., sensor reports) using domain-general strategies, may accurately and efficiently handle incomplete evidence, noisy evidence, and an incomplete knowledge base. I propose the following answer to the question. The agent should employ an optimal abductive reasoning algorithm (developed piece-wise and shown to be best in a class of similar algorithms) that allows it to reason from evidence to causes. For the sake of efficiency and operational concerns, the agent should establish beliefs periodically rather than waiting until it has obtained all evidence it will ever be able to obtain. If the agent commits to beliefs on the basis of incomplete or noisy evidence or an incomplete knowledge base, these beliefs may be incorrect. Future evidence obtained by the agent may result in failed predictions or anomalies. The agent is then tasked with determining whether it should retain its beliefs and therefore discount the newly-obtained evidence, revise its prior beliefs, or expand its knowledge base (what can be described as anomaly-driven or explanation-based learning). When the agent is considering whether its failed predictions or anomalies are the result of false beliefs or limitations in its knowledge, or instead the result of incomplete or noisy sensor reports, the agent is performing a kind of metareasoning, or reasoning about its own reasoning (Schmill et al. 2011). My approach treats this metareasoning procedure as itself abductive. When faced with failed predictions or anomalies, the agent attempts to explain its potential failure of reasoning. Possible explanations are that the agent committed to incorrect beliefs based on prior misleading evidence. Or, the newly-obtained evidence is misleading and the agent does not possess incorrect beliefs. A further explanation is that the agent’s knowledge base is incomplete, and that the anomaly resulted from the agent not having the proper facts about what kinds of events are possible in the world. The abductive metareasoning procedure (which utilizes the same abductive inference algorithm as the firstlevel reasoning procedure) produces its best explanation. Based on this explanation, the agent may attempt to repair its beliefs, ignore the newly-obtained evidence, or expand its knowledge base. These “fixes,” such as expanding its knowl-