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Matthew Molineaux

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5 papers

1 author row

AAAI Conference 2014 Conference Paper

Learning Unknown Event Models

Matthew Molineaux
David Aha

Agents with incomplete environment models are likely to be surprised, and this represents an opportunity to learn. We investigate approaches for situated agents to detect surprises, discriminate among different forms of surprise, and hypothesize new models for the unknown events that surprised them. We instantiate these approaches in a new goal reasoning agent (named FOOLMETWICE), investigate its performance in simulation studies, and report that it produces plans with significantly reduced execution cost in comparison to not learning models for surprising events.

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AAMAS Conference 2012 Conference Paper

DiscoverHistory: Understanding the Past in Planning and Execution

Matthew Molineaux
Ugur Kuter
Matthew Klenk

We consider the problem of automated planning and control for an execution agent operating in environments that are partially-observable with deterministic exogenous events. We describe a new formalism and a new algorithm, \textsc{DiscoverHistory}, that enables our agent, DHAgent, to proactively expand its knowledge of the environment during execution by forming explanations that reveal information about the world. We describe how DHAgent uses this information to improve the projections made during planning. Finally, we present an ablation study that examines the impact of explanation generation on execution performance. The results of this study demonstrate that our approach significantly increases the goal achievement success rate of DHAgent against an ablated version that does not perform explanation.

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AAAI Conference 2010 Conference Paper

Goal-Driven Autonomy in a Navy Strategy Simulation

Matthew Molineaux
Matthew Klenk
David Aha

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. We evaluate ARTUE on scenarios in the TAO Sandbox, a Navy training simulation, and demonstrate its novel architecture, which includes components for Hierarchical Task Network planning, explanation, and goal management. Our evaluation shows that ARTUE can perform well in a complex environment and that each component is necessary and contributes to the performance of the integrated system.

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AAAI Conference 2010 Conference Paper

Planning in Dynamic Environments: Extending HTNs with Nonlinear Continuous Effects

Matthew Molineaux
Matthew Klenk
David Aha

Planning in dynamic continuous environments requires reasoning about nonlinear continuous effects, which previous Hierarchical Task Network (HTN) planners do not support. In this paper, we extend an existing HTN planner with a new state projection algorithm. To our knowledge, this is the first HTN planner that can reason about nonlinear continuous effects. We use a wait action to instruct this planner to consider continuous effects in a given state. We also introduce a new planning domain to demonstrate the benefits of planning with nonlinear continuous effects. We compare our approach with a linear continuous effects planner and a discrete effects HTN planner on a benchmark domain, which reveals that its additional costs are largely mitigated by domain knowledge. Finally, we present an initial application of this algorithm in a practical domain, a Navy training simulation, illustrating the utility of this approach for planning in dynamic continuous environments.

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AAAI Conference 2005 System Paper

TIELT: A Testbed for Gaming Environments

Matthew Molineaux
David W. Aha

Many AI researchers want to test the utility of their systems in complex task environments defined by (e.g., real-time strategy) gaming simulators and/or simulators of computergenerated forces. Also, many developers of commercial and military gaming simulators seek behaviors that can be supported by these systems. However, these integrations require great effort. We will demonstrate the late Alpha version of TIELT, a testbed designed to fill these needs.

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