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K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 1
Behavior Recognition and Behavior Recognition and Opponent Modeling inOpponent Modeling in
Autonomous Multi-Robot SystemsAutonomous Multi-Robot Systems
Keith J. O’HaraCollege of Computing
Georgia Institute of [email protected]
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 2
IntroductionIntroduction
• 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
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 3
OutlineOutline
• 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.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 4
Important ThemesImportant 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.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 5
““Recognizing Multi-Agent Recognizing Multi-Agent Action from Visual Evidence”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:– ComplexComplex: partially ordered causal events– Multi-agentMulti-agent: parallel event streams– Uncertain: Uncertainty in Uncertain: Uncertainty in both data and model
• Other domainsOther domains– Sports, military, traffic, roboticsSports, military, traffic, robotics
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 6
MethodMethod
• 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.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 7
Temporal Structure DescriptionsTemporal Structure Descriptions
• Individual Goal• Action Components• Object Assignment• Temporal Constraints
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 8
Visual NetworksVisual Networks
• Construct belief network (visual network) based upon visual evidence.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 9
Multi-Agent BeliefMulti-Agent Belief Network Network
• Multi-Agent Networks normally contain at least 50 belief nodes and 40 evidence nodes
• Conditional and prior probabilities are determined automatically
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 10
ResultsResults• 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).
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 11
““Automated Robot Behavior Automated Robot Behavior Recognition”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.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 12
• 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, )
Method(1)Method(1)
s1 s2 s3
s4
O1 O2, O3 O3
O1
Go-To-Ball
O1O2O3
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 13
• The BHMM()– Set of States
– Observations Space
– State Transition Matrix
– Observation Probabilities
– Initial State Distribution
Method(2)Method(2)
s1 s2 s3
s4
O1 O2, O3 O3
O1
Go-To-Ball
O1O2O3
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K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 14
ResultsResults
• 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.
K. J. O’Hara AMRS: Behavior Recognition and Opponent Modeling
Oct 10 2002 15
ConclusionsConclusions
• New and hard problem.• Use of probabilistic techniques to deal with
uncertainty in perception and the plan.• Completely described action and observation
spaces.