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Goel, Ashok K.

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    Model-Based Reconfiguration of Schema-Based Reactive Control Architectures
    (Georgia Institute of Technology, 1997) Chen, Zhong ; Goel, Ashok K. ; Rowland, Paul ; Stroulia, Eleni
    Reactive methods of control get caught in local minima. Fortunately schema-based reactive control systems have built-in redundancy that enables multiple configurations with different modes. We describe a model-based method that exploits this redundancy, and, under certain conditions, reconfigures schema-based reactive control systems trapped in behavioral cycles due to the presence of local minima. The qualitative model specifies the functions and modes of the perceptual and motor schemas, and represents the reactive architecture as a structure-behavior-function model. The model-based method monitors the reactive processing, detects failures in the form of behavioral cycles, analyzes the processing trace, identifies potential modifications, and reconfigures the reactive architecture. We report on experiments with a simulated robot navigating a complex space.
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    Some Experimental Results in Multistrategy Navigation Planning
    (Georgia Institute of Technology, 1995) Goel, Ashok K. ; Ali, Khaled Subhi ; Stroulia, Eleni
    Spatial navigation is a classical problem in AI. In the paper, we examine three specific hypotheses regarding multistrategy navigation planning in visually engineered physical spaces containing discrete pathways: (1) For Hybrid robots capable of both deliberative planning and situated action, qualitative representations of topological knowledge are sufficient for enabling effective spatial navigation; (2) For deliberative planning, the case-based strategy of plan reuse generates plans more efficiently than the model-based strategy of search without any loss in the quality of plans or problem-solving coverage; and (3) For the strategy of model-based search, the “principle of locality” provides a productive basis for partitioning and organizing topological knowledge. We describe the design of a multistrategy navigation planner called Router that provides an experimental testbed for evaluation the three hypotheses. We also describe the embodiment of Router on a mobile robot called Stimpy for testing the first hypothesis. Experiments with Stimpy indicate that this hypothesis apparently is valid for hybrid robots in visually engineered spaces containing discrete pathways such as office buildings. In addition, two different kinds of simulation experiments with Router indicate that the second and the third hypotheses are only partially correct. Finally, we relate the evaluation methods and experimental designs with the research hypotheses.
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    Reasoning About Function in Reflective Systems
    (Georgia Institute of Technology, 1993) Stroulia, Eleni ; Goel, Ashok K.
    Functional models have been extensively investigated in the context of several problem-solving tasks such as device diagnosis and design. In this paper, we view problem solvers themselves as devices, use functional models to represent how they work, and subsequently employ these models for performance-driven reflective reasoning and learning. We represent the functioning of a problem solver as a structure-behavior-function model that specifies how the knowledge and reasoning of the problem solver results in the achievement of its goals. We view performance-driven learning as the task of redesigning the knowledge and reasoning of the problem solver. We use the structure-behavior-function model of the problem solver to monitor its reasoning, reflectively assign blame when it fails, and redesign its knowledge and reasoning. This paper describes an architecture for reflective model-based reasoning that is capable of a broad range of learning tasks. It also illustrates reflective model-based learning using examples from the Autognostic system, a reflective path planner.