Modern complex games and simulations pose many challenges for an intelligent agent, including partial observability, continuous time and effects, hostile opponents, and exogenous events. We present ARTUE (Autonomous Response to Unexpected Events), a domain-independent autonomous agent that dynamically reasons about what goals to pursue in response to unexpected circumstances in these types of environments. ARTUE integrates AI research in planning, environment monitoring, explanation, goal generation, and goal management. To explain our conceptualization of the problem ARTUE addresses, we present a new conceptual framework, goal-driven autonomy, for agents that reason about their goals.

Abstract. This paper seeks to extend notions of monitoring in metareasoning to include symbolic and linguistic expressions of self for purposes of communication and learning. The essay is intended to present a synthesis in plain language that challenges the agent community interested in metareasoning to consider what it means for a system to understand itself in any meaningful way. The basic claim is that if an agent truly knows what it is doing and why, it should be able explain itself to others using natural language or some other interactive mechanism with humans. To perform self-explanation it must be able to understand itself, and for this to occur it must monitor its own metareasoning and have an episodic memory that forms the basis of self. A further challenge is to incorporate self-explanation into an evaluation function that complements criteria based solely on action performance. 1

Abstract. The vast majority of research on AI planning has focused on automated plan recognition, in which a planning agent is provided with a set of inputs that include an initial goal (or set of goals). In this context, the goal is presumed to be static; it never changes, and the agent is not provided with the ability to reason about whether it should change this goal. For some tasks in complex environments, this constraint is problematic; the agent will not be able to respond to opportunities or plan execution failures that would benefit from focusing on a different goal. Goal driven autonomy (GDA) is a reasoning framework that was recently introduced to address this limitation; GDA systems perform anytime reasoning about what goal(s) should be satisfied [4]. Although promising, there are natural roles that case-based reasoning (CBR) can serve in this framework, but no such demonstration exists. In this paper, we describe the GDA framework and describe an algorithm that uses CBR to support it. We also describe an empirical study with a multiagent gaming environment in which this CBR algorithm outperformed a rule-based variant of GDA as well as a non-GDA agent that is limited to dynamic replanning. 1

Dynamic changes in complex, real-time environments, such as modern video games, can violate an agent’s expectations. We describe a system that responds competently to such violations by changing its own goals, using an algorithm based on a conceptual model for goal driven autonomy. We describe this model, clarify when such behavior is beneficial, and describe our system (which employs an HTN planner) in terms of how it partially instantiates and diverges from this model. Finally, we describe a pilot evaluation of its performance for controlling agent behavior in a team shooter game. We claim that the ability to selfselect goals can, under some conditions, improve plan execution performance in a dynamic environment.

Abstract. We describe our progress on instrumenting a Navy software simulator for use in the context of intelligent agent research. The Tactical Action Officer (TAO) Sandbox, developed at the University of Southern California, is used by officers to train for specific Navy missions. NRL and Knexus Research Corporation have integrated this simulator with intelligent agents using the Lightweight Integration and Evaluation Testbed (LIET), thus permitting the agent to play the role of a trainee. This will permit us to use the TAO Sandbox in our artificial intelligence research, where we are currently focusing on algorithms for continuous planning that can dynamically reason about what goal should be pursued at any time during a mission. This paper briefly descibes our motivation for this integration, project status involving this simulator, and future goals. 1.

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