Michael Cox

AAAI Conference 2017 Conference Paper

Goal Operations for Cognitive Systems

• Michael Cox
• Dustin Dannenhauer
• Sravya Kondrakunta

Cognitive agents operating in complex and dynamic domains benefit from significant goal management. Operations on goals include formulation, selection, change, monitoring and delegation in addition to goal achievement. Here we model these operations as transformations on goals. An agent may observe events that affect the agent’s ability to achieve its goals. Hence goal transformations allow unachievable goals to be converted into similar achievable goals. This paper examines an implementation of goal change within a cognitive architecture. We introduce goal transformation at the metacognitive level as well as goal transformation in an automated planner and discuss the costs and benefits of each approach. We evaluate goal change in the MIDCA architecture using a resource-restricted planning domain, demonstrating a performance benefit due to goal operations.

AAAI Conference 2016 Conference Paper

MIDCA: A Metacognitive, Integrated Dual-Cycle Architecture for Self-Regulated Autonomy

• Michael Cox
• Zohreh Alavi
• Dustin Dannenhauer
• Vahid Eyorokon
• Hector Munoz-Avila
• Don Perlis

We present a metacognitive, integrated, dual-cycle architecture whose function is to provide agents with a greater capacity for acting robustly in a dynamic environment and managing unexpected events. We present MIDCA 1. 3, an implementation of this architecture which explores a novel approach to goal generation, planning and execution given surprising situations. We formally define the mechanism and report empirical results from this goal generation algorithm. Finally, we describe the similarity between its choices at the cognitive level with those at the metacognitive.

IJCAI Conference 2007 Conference Paper

• Alice M. Mulvehill
• Brett Benyo
• Michael Cox
• Renu Bostwick

Plans provide an explicit expectation of future observed behavior based upon the domain knowledge and a set of action models available to a planner. Incorrect or missing models lead to faulty plans usually characterized by catastrophic goal failure. Non-critical anomalies occur, however, when actual behavior during plan execution differs only slightly from expectations, and plans still achieve the given goal conjunct. Such anomalies provide the basis for model adjustments that represent small adaptations to the planner's background knowledge. In a multi-agent environment where 1000 or more individual plans can be executing at any one time, automation is required to support model anomaly detection, evaluation, and revision. We provide an agent-based algorithm that generates hypotheses about the cause of plan anomalies. This algorithm leverages historical plan data and a hierarchy of models in a novel integration of hypothesis generation and verification. Because many hypotheses can be generated by the software agents, we provide a mechanism where only the most important hypotheses are presented to a user as suggestions for model repair.