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Behavior Coordination Mechanisms – State-of-the- Art Paper by: Paolo Pirjanian (USC) Presented by: Chris Martin
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Subject of Paper Overview of different Action Selection Mechanisms (ASMs) that “solve” the Action Selection Problem (ASP) A major issue in behavior-based control (BBC) systems is the formulation of effective mechanisms for coordination of the behaviors’ activities into strategies for rational and coherent behavior
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Research Areas Tackling ASP Ethology Artificial Life (AL) Virtual Reality (VR) Software Agents Robotics (Physical Agents)
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What is the ASP? Deals with the agent selecting “the most appropriate” or “the most relevant” next action at a particular moment in a particular situation Choosing a “good enough” or “satisficing” behavior
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“Good enough” behaviors According to Mae’s, to produce a “good enough” behavior, need the following requirements Goal orientedness Situatedness Persistence Planning Robustness reactivity
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“Good enough” behaviors (2) Tyrell adds the following to Mae’s list: Deal with all types of subproblems Compromise actions Opportunism (in contrast to persistence)
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Actions and Agents Based on the Webster dictionary, two relevant definitions of action for two parts of the system: 1. “the condition of acting or moving, as opposed to rest” = motor movements 2. “habitual deeds; hence conduct; behavior” = activation of a behavior Agents have 2 roles in an agency: actions and action selection mechanisms (ASMs) With respect to its subordinates, agent is ASM With respect to its superior, agent is action
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Types of ASMs Two types: Arbitration and Command Fusion (CF) ASMs One characteristic defines the division: the number of behaviors handled Arbitrary ASMs allow one or one set of behaviors to take control at any one time Command Fusion ASMs allow multiple behaviors to contribute to final control of the robot
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Arbitration ASMs – 3 Types 1. Priority-based Action selection consists of higher level behaviors overriding the output of lower level behaviors 2. State-based -- 4 different examples A. Discrete Event Systems Behavior selection done using state transitions Detection of certain events shift the system to a new state and new behavior
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Arbitration ASMs (2) State-based cont. B. Temporal Sequencing At each state a behavior is activated and perceptual triggers cause state transition C. Bayesian Decision Analysis Choose action that maximizes expected utility of agent (cost/benefit) D. Reinforcement Learning Two kinds: Hierarchical Q-learning: problem broken into smaller problems each learned separately through Q-learning W-learning: each module/behavior recommends an action with some weight and action with highest weight selected and executed
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Arbitration ASMs (3) 3. Winner-Take-All -- one example Activation Networks A set of behaviors reduce the difference between the system’s present state and goal state By exchange of activation energy, the behaviors compete and cooperate to select an action The system emergently chooses and performs the next step of the sequence
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Command Fusion ASMs Combine recommendations from multiple behaviors to form a control action that represents a consensus Proceeds in 3 steps Action recommendations Behavior aggregation Action selection
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CF ASMs – 4 Types 1. Voting -- 3 examples A. DAMN Behavior votes for or against set of actions Each behavior assigned weight by mode manager ‘Voter’ selects ‘best’ action Experiments show DAMN superior to other ASMs
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CF ASMs – 4 Types (2) Voting cont. B. SAMBA Primitive behaviors produce reactions in form of primitive action maps Behavior outputs generated from 4 primitive action maps Command arbiter combines maps by multiplying each by a gain and adding the results C. Action Voting Each behavior votes for an action and votes against undesirable actions The votes are summed and action with highest value is selected
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CF ASMs – 4 Types (3) 2. Fuzzy Command Fusion -- 2 examples A. Fuzzy/Multivalued Logic Approach Control schema Behavior schema Planners B. Fuzzy DAMN Outputs of behaviors are cast as discrete membership functions over the set of possible actions Weighted sum replaced with fuzzy inferencing methods Max-vote replaced with defuzzifcation techniques
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CF ASMs – 4 Types (4) 3. Multiple Objective Behavior Coordination (MOBC) Each behavior calculates an objective function over a set of permissible actions Action that maximizes the objective function is the best “satisficing” objective Action selection is comprised of generating and then selecting a set of “satisficing” solutions among a set of efficient solutions known as Pareto-optimal solutions
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CF ASMs – 4 Types (5) 4. Superposition Based Command Fusion -- 2 examples A. Potential Fields Approach to motion planning Robot moves under the influence of an artificial potential field produced by an attractive force at the goal configuration and repulsive forces at obstacles B. Motor Schemas Generates a vector which encodes the direction and intensity of motor action (calculates a potential field for current configuration of the robot) These vectors are added to generate a combined motor action This is then multiplied by a gain value Used on AuRA
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