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Human Directability of Agents Karen Myers, David Morley {myers, AI Center SRI International.

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Presentation on theme: "Human Directability of Agents Karen Myers, David Morley {myers, AI Center SRI International."— Presentation transcript:

1 Human Directability of Agents Karen Myers, David Morley {myers, AI Center SRI International

2 True Confessions Why am I here? 1.Directing Agents: learning by being told 2.Critical need for learning technology to develop real-world agent applications *** I am not a Machine Learning Person ***

3 3/20/2004K.L. Myers SRI International AAVs Smart Cockpit Smart Home/Office Robot Teams Networks Spacecraft SoftBots Agents Everywhere!

4 Current Practice Objective: mixed-initiative directability of agents by a human supervisor Delegation without loss of control Fully Autonomous Agent makes all decisions Ex: mobile robots Teleoperation Human makes all decisions Ex: internet agents, UCAVs 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 Interaction Spectrum

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 Disaster Relief Intel Management TRAC Supervisor controlleduncontrolled Coordinator Agent Truck Agents Heli Agents Comms Agent MAPLESIM

7 BDI Agent Model (a la PRS) Executor Plan Library Tasks IntentionsBeliefs User 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 1.Language for expressing guidance Belief-Desire-Intention (BDI) Model of Agency FOL Domain Metatheory 2.Formal Semantics Guidance-compatible execution 3.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 BDI ConstructAgent Context Equivalent Beliefsconditions that must be believed true Desiresdesire specifications for tasks Intentionsactivity specification for intended plans 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 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 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 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 P5P5 P1P1 P3P3 P2P2 P4P4 Good Bad Filter-based Semantics 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

22 Guidance Conflicts (1) A. Plan Selection: guidance yields contradictory suggestions –Execute Plan P / Dont execute Plan P P5P5 P1P1 P3P3 P2P2 P4P4 Good Bad P5P5 P1P1 P3P3 P2P2 P4P4 RankingRanking Filter-based SemanticsPrioritized Semantics Solution –Rank applicable plans according to guidance satisfaction –Select higher-ranked plan(s) when there is a conflict

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 P5P5 P1P1 P3P3 P2P2 P4P4 Good Bad P5P5 P1P1 P3P3 P2P2 P4P4 RankingRanking P6P6 P7P7 P8P8 Filter-based SemanticsPrioritized Expansion Semantics Solution –Expand the set of candidate plans proactively Resolution Plans: Delay current task to obtain required resource

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

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) 1. Learning by Being Told Human Guidance for Agents (Myers, Morley) Interactive Acquisition/Modification of Procedures (Blythe) 2. Preference Learning for Management (Gervasio) folder and priority prediction 3. Preference Learning for Calendar Management (Gervasio) Schedule evaluation functions 4. Reinforcement Learning for Reminder Customization (Pollack) 5. 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 1.Programming by demonstration Calendar Manager: how to arrange meetings of different types Observe sequence of actions from meeting initiation to actual meeting 2.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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