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Cognitive Science Integrating a Closed World Planner with an Open World Robot: A Case Study 1 Kartik Talamadupula J. Benton Subbarao Kambhampati Dept. of Computer Science Arizona State University Paul Schermerhorn Matthias Scheutz Cognitive Science Program Indiana University
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Cognitive Science Urban Search & Rescue 2 Human-Robot team Robot starts at beginning of hallway Hard Goal Reach the end of the hallway Wounded people in rooms Soft Goal Report the locations of wounded people In communication with the human
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Cognitive Science 3 Planner Robot Closed World Open World Under Sensing Closed World Model Limited Sensing Planner guides robot in a limited way Over Sensing Robot senses its way through the world How do you make a deterministic closed-world planner believe in opportunities sans guarantees? Open World Quantified Goals Partial Satisfaction Planning (PSP) Sensing and Replanning
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Cognitive Science Bias the Planner’s Model Endow the planner with an optimistic view – Assume existence of objects and facts that may lead to rewarding goals e.g. the presence of a victim in a room – Create runtime objects – Add to the planner’s database of ground objects Plans are generated over this reconfigured potential search space 4 Some people see things as they are and say why? I dream things that never were and say why not? --(mis)attributed to Robert Kennedy (who quoted Bernard Shaw) planners Our planner dreams s
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Cognitive Science Open World Quantified Goals (OWQGs) Goals that allow for the specification of additional information – To take advantage of opportunities 5 (:open (forall ?r – room (sense ?p – person (looked_for ?p ?r) (and (has_property ?p wounded) (in ?p ?r)) (:goal (and (reported ?p wounded ?r) [100] - soft)))) Quantified Object(s) Sensed Object Closure Condition Quantified Facts Quantified Goal
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Cognitive Science Partial Satisfaction 6 Opportunities retain their bonus nature Not enough time (or other metric resources) to fulfill all goals Sensing has a cost Rewards from object existence SOFT GOALS QUANTIFIED GOALS NET BENEFIT
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Cognitive Science Planning to Sense Sensing to Plan Sensing is expensive … Cannot be done at every step Planner needs to direct the architecture on: when to sense what to sense for Planning to sense in a goal-directed manner Output all actions up to (and including) any action that results in sensing closure Tempers the optimism created in the planner by the OWQGs 7 Goal Manager Monitor Planner Plan Problem Updates Updated State Information Robot
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Cognitive Science Evaluation Evaluated on the USAR scenario Experimental Setup – Robot in a corridor; hard goal to reach the end. – Three rooms with green, blue and no box respectively. Hard goal has a timed deadline Planner reports wounded people in rooms 8
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Cognitive Science Lack of support for uncertainty – Uncertainty about fact distribution – E.g. Wounded people more likely in bathrooms Determinization is too optimistic – Object existence assumed – Wumpus World: A wumpus in every room? Limitations & Extensions Augment OWQGs to handle probabilities – Generate more than one outcome (FF-HOP) Precautionary Planning – Replan for execution failures – Add precautionary measures to deal with unrecoverable failures 9 LIMITATIONSeXTENSIONS
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Cognitive Science Related Work Design rewards such that reactive behavior leads to the optimal goal [Marthi 2007] – Sprinkle donuts on the road liberally to lure Homer Simpson into the library Local Closed World statements [Etzioni et al. 1997] – Completely open world – Parts of the planner’s representation of the world closed via LCW statements – Our approach is complementary OWQGs open parts of a closed world 10
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Cognitive Science 11 Planner Robot Closed World Open World How did we make a deterministic closed-world planner believe in opportunities (without guarantees)? Open World Quantified Goals Summary Partial Satisfaction Planning 1. Maintain bonus nature 2. Quantified goals 3. Limit sensing actions Sensing & Replanning 1. Temper planner’s optimism 2. Replan for unexpected states and new information
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