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Behavior Recognition and Opponent Modeling in Autonomous Multi-Robot Systems

Behavior Recognition and Opponent Modeling in Autonomous Multi-Robot Systems. Keith J. O’Hara College of Computing Georgia Institute of Technology kjohara@cc.gatech.edu. Introduction. Recognizing and modeling behavior from low-level action thru high-level strategy.

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Behavior Recognition and Opponent Modeling in Autonomous Multi-Robot Systems

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  1. Behavior Recognition and Opponent Modeling inAutonomous Multi-Robot Systems Keith J. O’Hara College of Computing Georgia Institute of Technology kjohara@cc.gatech.edu

  2. Introduction • Recognizing and modeling behavior from low-level action thru high-level strategy. • Single agent primitive action • A sequence of single agent actions • Group behavior • To understand opponents • To understand teammates • No Communication • Communication troublesome or dangerous • Speak different “languages” • Operate based on a different behavior vocabulary

  3. Outline • 2 Approaches • Intille and Bobick (MIT) • Application of bayesian belief networks for American football play recognition. • Han and Veloso (CMU) • Behavior Hidden Markov Models for robot soccer behavior recognition.

  4. Important Themes • Single/Multi agent • Recognition of agents and primitive actions • Agent subgoals, goals, intentions • Group subgoals, goals, intentions • Online recognition • Uncertainty in Perception • Uncertainty/Flexibility of Plan • Use of probabilistic techniques to deal with uncertainty. • Completely described action and observation spaces.

  5. “Recognizing Multi-Agent Action from Visual Evidence” • Recognition of American football plays from real games. • Assumes we have labeled participants with rough position and orientation estimates. • Properties of the domain: • Complex: partially ordered causal events • Multi-agent: parallel event streams • Uncertain: Uncertainty in both data and model • Other domains • Sports, military, traffic, robotics

  6. Method • Method inspired by model-based object recognition techniques. • Database of plays (temporal structure descriptions) described by temporal and logical relationships of events. • Construct “visual network” to detect individual goals (primitive actions) from visual evidence.

  7. Temporal Structure Descriptions • Individual Goal • Action Components • Object Assignment • Temporal Constraints

  8. Visual Networks • Construct belief network (visual network) based upon visual evidence.

  9. Multi-Agent Belief Network • Multi-Agent Networks normally contain at least 50 belief nodes and 40 evidence nodes • Conditional and prior probabilities are determined automatically

  10. Results • System of 29 tracked plays, 10 temporal play descriptions • 21/25 were recognized correctly • False positives are a problems. (plays that aren’t defined) • Recognized single-agent behavior and multi-agent plays. • Handled fuzzy temporal relationships (around, before). • Not evaluated online. • Assumes tracking/labeling/localization problem is solved. (Manually done in this work.) • Must know entire domain of observations (player states), and all possible plans (play book).

  11. “Automated Robot Behavior Recognition” • Robot Soccer • Adaptable Strategy • Narrative Agents • Coaches • Formalism • Agent R is the observed robot • Agent O is the observing robot • R acts according to a known set of behaviors h(i) • O has a model of the set of the possible behaviors. • O must decide which h(i), R is performing. • Must be online algorithm. • One observed robot and one observed ball.

  12. Go-To-Ball s1 s2 s3 O1 O2, O3 O3 s4 O1 O2 O1 O3 Method(1) • Use Hidden Markov Models (HMMs) to recognize behaviors • Motivated by success of HMMs in other “recognition” tasks. (e.g. speech, gesture) • A Behavioral HMM() for each behavior • Set of States • Initial, intermediate, accept, reject • Observations Space • Absolute/Relative Position, Dynamic (velocity) • State Transition Matrix • Observation Probabilities • Initial State Distribution • P(this state | observations, )

  13. Go-To-Ball s1 s2 s3 O1 O2, O3 O3 s4 O1 O2 O1 O3 Method(2) • The BHMM() • Set of States • Observations Space • State Transition Matrix • Observation Probabilities • Initial State Distribution

  14. Results • Online algorithm • Applied to robotics domain (simulation/real-robots) • Implemented everyone’s favorite behaviors • Go-To-Ball, Go-Behind-Ball, Intercept-Ball, Goalie-Align-Ball • Not much quantitative evidence. • Only single agent case. • Assume each behavior to be a sequence of state traversals. • BHMM and behavior initial states must match up, or use a timeout/restart mechanism. • Mentioned by Intille and Bobick as a problem with treating temporal constraints as first-order markovian.

  15. Conclusions • New and hard problem. • Use of probabilistic techniques to deal with uncertainty in perception and the plan. • Completely described action and observation spaces.

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