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Iterative Goal Refinement for Robotics
"... Goal Reasoning (GR) concerns actors that assume the responsibility for dynamically selecting the goals they pursue. Our focus is on modelling an actor’s decision making when they encounter notable events. We model GR as an iterative refinement process, where constraints introduced for each abstracti ..."
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Goal Reasoning (GR) concerns actors that assume the responsibility for dynamically selecting the goals they pursue. Our focus is on modelling an actor’s decision making when they encounter notable events. We model GR as an iterative refinement process, where constraints introduced for each abstraction layer shape the solutions for successive layers. Our model provides a conceptual framework for robotics researchers and practitioners. We present a goal lifecycle and define a formal model for GR that (1) relates distinct disciplines concerning actors that operate on goals, and (2) provides a way to evaluate actors. We introduce GR using an example on waypoint navigation and outline its application, in three projects, for controlling simulated and real-world vehicles. We emphasize the relation of GR to planning, and encourage PlanRob researchers to collaborate in exploring this exciting frontier. 1.
Towards Applying Goal Autonomy for Vehicle Control
"... Unmanned vehicles have been the focus of active research on autonomous motion planning, both deliberative and reactive. However, they are fundamentally limited in their autonomy by an inability to independently reason about, prioritize, and change the goals they pursue. We describe two new projects ..."
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Unmanned vehicles have been the focus of active research on autonomous motion planning, both deliberative and reactive. However, they are fundamentally limited in their autonomy by an inability to independently reason about, prioritize, and change the goals they pursue. We describe two new projects in which we are incorporating goal autonomy on unmanned vehicle platforms. We will apply the Goal-Driven Autonomy (GDA) model to permit our vehicles to reason about their objectives and discuss how properties of the domains affect the application of GDA. 1.
Goal Reasoning: Papers from the ACS Workshop
, 2013
"... Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its ..."
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Our objective for holding this meeting was to encourage researchers to share information on the study, development, integration, evaluation, and application of techniques related to goal reasoning, which concerns the ability of an intelligent agent to reason about, formulate, select, and manage its goals/objectives. Goal reasoning differs from frameworks in which agents are told what goals to achieve, and possibly how goals can be decomposed into subgoals, but not how to dynamically and autonomously decide what goals they should pursue. This constraint can be limiting for agents that solve tasks in complex environments when it is not feasible to manually engineer/encode complete knowledge of what goal(s) should be pursued for every conceivable state. Yet, in such environments, states can be reached in which actions can fail, opportunities can arise, and events can otherwise take place that strongly motivate changing the goal(s) that the agent is currently trying to achieve. This topic is not new; researchers in several areas have studied goal reasoning (e.g., in the context of cognitive architectures, automated planning, game AI, and robotics). However, it has infrequently been the focus of intensive study, and (to our knowledge) no other series of meetings has focused specifically
Goal-Driven Autonomy and Question-Based Problem Recognition
"... Autonomy involves not only the capacity to achieve the goals one is given but also to recognize problems and to generate new goals of one’s own that are worth achieving. The creation of goals starts with the recognition of a novel problem, and this recognition begins with the detection of anomalies ..."
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Autonomy involves not only the capacity to achieve the goals one is given but also to recognize problems and to generate new goals of one’s own that are worth achieving. The creation of goals starts with the recognition of a novel problem, and this recognition begins with the detection of anomalies represented as the difference between expectations and observations (or inferences). The expectation failure triggers the posing of questions; questions lead to explanations; and explanations form the basis for goals. I illustrate these principles with examples from a computational architecture called MIDCA. 1.
