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. 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

The ARTUE (Autonomous Response to Unexpected Events) system was built as a prototype to demonstrate the usefulness of Goal-Directed Autonomy. We provide an overview of some of the design decisions made in its construction, as well as a discussion of how we chose to evaluate it. We close with a brief discussion of interesting research questions raised by ARTUE’s design.

We present a drive-based agent capable of playing the realtime strategy computer game Starcraft. Success at this task requires the ability to engage in autonomous, goal-directed behaviour, as well as techniques to manage the problem of potential goal conflicts. To address this, we show how a caseinjected genetic algorithm can be used to learn goal priority profiles for use in goal management. This is achieved by learning how goals might be re-prioritised under certain operating conditions, and how priority profiles can be used to dynamically guide high-level strategies. Our dynamic system shows greatly improved results over a version equipped with static knowledge, and a version that only partially exploits the space of learned strategies. However, our work raises questions about how a system must know about its own design in order to best exploit its own competences.