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Human Directability of Agents

Human Directability of Agents. Karen Myers, David Morley {myers, morley}@ai.sri.com AI Center SRI International. True Confessions. *** I am not a Machine Learning Person ***. Why am I here? Directing Agents: learning by being told

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Human Directability of Agents

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  1. Human Directability of Agents Karen Myers, David Morley {myers, morley}@ai.sri.com AI Center SRI International

  2. True Confessions *** I am not a Machine Learning Person *** Why am I here? • Directing Agents: learning by being told • Critical need for learning technology to develop real-world agent applications

  3. Spacecraft AAVs Networks Smart Cockpit Smart Home/Office Robot Teams Agents Everywhere! SoftBots K.L. Myers SRI International

  4. Current Practice Interaction Spectrum Teleoperation Human makes all decisions Ex: internet agents, UCAVs Fully Autonomous Agent makes all decisions Ex: mobile robots  Acts according to human preferences  Little knowledge modeling needed X Human bears cognitive load Little human influence X Must encode all expertise X Low human cognitive load  Objective: mixed-initiative directability of agents by a human supervisor • Delegation without loss of control

  5. Supervised Autonomy Scope of applicability • Agents capable of fully autonomous operation • Want agents to be mostly autonomous • Human influence would improve performance • Humans want to customize agent operations Approach • Dynamic guidance for management of agents • Strategy Preference • Adjustable Autonomy

  6. Supervisor Coordinator Agent Comms Agent Truck Agents Heli Agents controlled uncontrolled Disaster Relief Intel Management TRAC MAPLESIM

  7. BDI Agent Model (a la PRS) User Plan Library Tasks Executor Beliefs Intentions World

  8. Strategy Preference Strategy: how to make decisions Assumption: agents have library of parameterized plans Approach:guidance defines policies on plan selection, parameter instantiation • Example • Only use helicopters for survey tasks in sectors more than 200 miles from base.

  9. Adjustable Autonomy Autonomy: degree to which agent makes its own decisions Assumption: agents capable of full autonomy Approach:guidance restricts space of agent decisions • Permission Requirements gating conditions on actions • Obtain permission before abandoning survey tasks with Priority>3 • Consultation Requirements deferred choice • Consult me when selecting locations for evacuation sites.

  10. Guidance Foundations • Language for expressing guidance • Belief-Desire-Intention (BDI) Model of Agency • FOL • Domain Metatheory • Formal Semantics • Guidance-compatible execution • Enforcement Methods • Operationalization within BDI interpreter loop

  11. Domain Metatheory Base-level Agent Theory • Individuals • Relations modeling the world, internal agent state • Tasks • Plans Domain Metatheory • Captures high-level, distinguishing attributes of plans, tasks • Features, Roles

  12. Example Domain Metatheory Feature - distinguishing attribute of a plan/task • Plans for Task: MOVE(Obj1 Place1 Place2) • Move-by-Land-Opr: LAND • Move-by-Sea-Opr:SEA • Move-by-Air-Opr:AIR Role - capacity in which a variable is used • Origin: Place.1, Destination: Place.2 Key Idea: abstraction over individual plans, tasks

  13. Guidance Components • Use domain metatheory to define abstract classes of plans, goals, and agent state • Activity specification • Desire specification • Agent context

  14. Activity Specification • Abstract characterization of a class of activities • Defined in terms of: • Features required/prohibited • Constraints on role values • Example: Abandon a survey task • Features: Abandon • Roles: Current-Task • Role Constraints: (= (TASK-TYPE Current-Task) SURVEY)

  15. Desire Specification • Abstract characterization of a class of desires • Defined/used similarly to Activity Specification

  16. Agent Context • Describes an operational state of agent • Example: Performing a communication plan for a Survey task within 10 miles of the Base • Beliefs:(< (Distance (Current-Position) Base) 10) • Desires: Features: Survey • Intentions: Features: Communication

  17. Permission Requirement Definition<agent-context, activity-specification> Semantics when in the context, permission is required to adopt plans that match the activity specification • Ex: Seek permission to abandon survey tasks with priority > 5 • Agent Context: • Intentions: Feature: SURVEY-TASK • Activity-Spec: • Features: ABANDON • Roles: Current-Task • Role Constraints: (> (Task-Priority Current-Task) 5)

  18. Consultation Requirement Definition<agent-context, role> Semantics when in the context, consult the supervisor when there are options for the designated role • Ex: When responding to medical emergencies, consult when selecting MedEvac facilities. • Agent Context: • Intention: • Features: Medical-Emergency, Response • Role: MedEvac-Facility

  19. Strategy Preference Definition<agent-context, activity-specification> Semantics when in the context, plans matching activity specification should be preferred • Ex: Respond to rescue emergencies involving more than 10 people when the severity exceeds the current task priority. • Agent Context: • Features: Emergency, Response • Roles: Current-Task, Severity, Number • Role Constraints: (AND (> Number 10) • (> Severity (TASK-PRIORITY Current-Task))) • Activity Specification: • Features: ADOPT • Roles: New-Task • Constraints: (= (TASK-PRIORITY New-Task) ESEVERITY.1)

  20. Guidance Interface Tools

  21. Guidance Enforcement Simple Semantics: guidance as filters on applicable plans Enforcement: • Simple extension to BDI executor • Modify plan selection step to incorporate • Filtering of plans with respect to guidance constraints • User consultation Filter-based Semantics Good P1 P3 Bad P5 P4 P2

  22. Guidance Conflicts (1) A.Plan Selection:guidance yields contradictory suggestions • Execute Plan P / Don’t execute Plan P • Solution • Rank applicable plans according to guidance satisfaction • Select higher-ranked plan(s) when there is a conflict Filter-based Semantics Prioritized Semantics Ranking Good P1 P3 P1 P5 P3 Bad P5 P4 P2 P4 P2

  23. Guidance Conflicts (2) B. Situated Conflict: prior activities block guidance application • Guidance would recommend a response to an emergency but required resources are unavailable • Solution • Expand the set of candidate plans proactively • Resolution Plans:Delay current task to obtain required resource Filter-based Semantics Prioritized Expansion Semantics Ranking P6 Good P1 P3 P1 P5 P3 P7 Bad P5 P4 P2 P8 P4 P2

  24. Related Work • Deontic logics • Obligation, permission, authority modalities • Mostly formal rather than practical • Policy-based systems management • Incorporating deontic concepts for runtime definition of behaviors • Sets authority parameters for components • Adjustable Autonomy • Electric-Elves: MDP based approach for consultation

  25. Summary • Technical Contributions: • Language, semantics, enforcement techniques for agent guidance • Form of ‘learning by being told’ --- limited to control rather than core knowledge • Benefits: • Combines capabilities of humans and agents • Adapts to dynamic user preferences • Reduced knowledge modeling effort • Status: • TRAC implementation on top of PRS; reimplementation in SPARK

  26. CALO: Cognitive Assistant the Learns and Organizes • Develop an intelligent personal assistant for a high-level knowledge worker • Large project encompassing ~20 different research organizations in the US; led by SRI • “Integrated Learning” as a key theme

  27. CALO Task Manager Timeline Introspect Interact Task Manager Plan Act Notice Anticipate t t Now • Capabilities: • Perform tasks on behalf of the user (reactively, proactively) • Manage user commitments (time, workload) • Keep the user informed • Coordinate interactions with other CALOs

  28. The Need for Integrated Learning • Capabilities • User customization • Extending/modifying procedural knowledge • Performance improvement • Setting • Learning unobtrusively • Learning from small number of cases (for some things) • Mixed-initiative setting

  29. Learning in the Task Manager (Current) • Learning by Being Told • Human Guidance for Agents (Myers, Morley) • Interactive Acquisition/Modification of Procedures (Blythe) • Preference Learning for Email Management (Gervasio) • folder and priority prediction • Preference Learning for Calendar Management (Gervasio) • Schedule evaluation functions • Reinforcement Learning for Reminder Customization (Pollack) • Query Relaxation via online data mining (Muslea) • mine small subset of solution space for rules that relate domain attributes; use the rules to relax query constraints

  30. Learning Procedural Knowledge • Programming by demonstration • Calendar Manager: how to arrange meetings of different types • Observe sequence of actions from meeting initiation to actual meeting • Failure-driven learning procedure adaptation (automated, mixed-initiative) • Adapt/extend predefined core of procedures to handle a broader set of tasks, improve robustness • User & Agent explore high-dimensional traces of failed tasks

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