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A Cognitive Framework for Delegation to an Assistive User Agent. Karen Myers and Neil Yorke-Smith Artificial Intelligence Center, SRI International. Overview. CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments - PowerPoint PPT Presentation
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A Cognitive Framework for Delegation to an Assistive User Agent
Karen Myers and Neil Yorke-SmithArtificial Intelligence Center, SRI International
Overview
CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments Summary and research issues
CALO: Cognitive Assistant that Learns and Organizes
CALO supports a high-level knowledge worker Understands the “office world”, your projects and schedule Performs delegated tasks on your behalf Works with you to complete tasks
Stays with you (and learns) over long periods of time Learns to anticipate and fulfill your needs Learns your preferred way of working
Track execution of project tasks
Help manage time and commitments
Perform tasksin collaboration with the user
CALO Year 2
Overview
CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments Summary and research issues
Delegation May Lead to Conflicts
Focus on delegation of tasks from user to CALO Not on tasks to be performed in collaboration One aspect of CALO’s role as intelligent assistant
CALO cannot act if conflicts over actions Conflicts in tasks
“purchase this computer on my behalf” “register me for the Fall Symposium”
Conflicts in guidance “always ask for permissions by email” “never use email for sensitive purchases”
Conflicts in User’s Desires
“I wish to be thin” “I wish to eat chocolate” But Richard Waldinger’s
scotch mocha brownies are full of calories
conflict between incompatible desires User’s desires conflict with each other Humans seem to have no problem with such conflicts
CALO must recognize and respond appropriately
Other Types of Conflicts
Current and new commitments Currently CALO is undertaking tasks to:
Purchase an item of computer equipment Register user for a conference
Now user tasks CALO to register for a second conference Set of new goals is logically consistent and coherent But infeasible because insufficient discretionary funds
Commitments and advice User tasks CALO to schedule visitor’s seminar in best
conference room Existing advice: “Never change a booking in the
auditorium without consulting me” New goal and existing advice are inconsistent
The BDI Framework
CALO’s ability to act is based on BDI framework Beliefs = informational attitudes about the world Desires = motivational attitudes on what to do Intentions = deliberative commitments to act
Realized in the SPARK agent system Hierarchical, procedural reasoning framework BDI components in SPARK represented as:
Facts (beliefs) Intentions (goals/intentions) Desires are not represented
Procedures are plans to achieve intentions
Desires vs. Goals
Both are motivational attitudes Desires may be neither coherent (with beliefs)
nor consistent (with each other) Goals must be both
Desires are ‘wishes’; goals are ‘wants’ “I wish to be thin and I wish to eat chocolate” “I want to have another of Richard’s brownies”
Desires lead to goals CALO’s primary desire: satisfy its user
Secondary desires→goals to do what user asks
‘BDI’ Agents are Really ‘BGI’
Decision theory emphasizes B and D AI agent theory emphasizes B and I In most BDI literature, ‘Desires’ and ‘Goals’ are
confounded In practice, focus is on:
goal and then intention selection option generation, and plan execution and scheduling
Focus has been much less on: deliberating over desires goal generation advisability
vital for CALO
The Problem with BGI
When Desires and Goals are unified into a single motivational attitude:
Can’t support conflicting D/G (and D/B) Hard to express goal generation Hard to diagnose and resolve conflicts
Between D/G and I, and between G, I, and plans Hard to handle conflicts in advice
How can CALO make sense of the user’s taskings in order to act upon them?
How can CALO recognize and respond to (potential) conflicts?
Overview
CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments Summary and research issues
Cognitive Models for Delegation
agent
GA
Belief Buser
(do assigned tasks)
user
Bagent
Desire
Goal
Duser Dagent
Guser GCagent
+ +
+
alignment
delegation
refinement
decisionmaking
goal adoption
Candidate Goals
Adopted Goals
satisfy all tasks
Delegative BDI Agent Architecture
user
failureconflicts
revision
advice AEAG
agent
GC GA I execute
B
sub-goaling
B
D
G
Candidate GoalsAdopted GoalsIntentions
Goal AdviceExecution Advice
Overview
CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments Summary and research issues
Requirements on Goal Adoption
Self-consistency: GA must be mutually consistent Coherence: GA must be mutually consistent
relative to the current beliefs B Feasibility: GA must be mutually satisfiable
relative to current intentions I and available plans Includes resource feasibility
Reasonableness: GA should be mutually ‘reasonable’ with respect to current B and I
Common sense check: did you really mean to purchase a second laptop computer today?
Responding to Conflicting Desires
Goal adoption process should admit: Adopting, suspending, or rejecting candidate goals Modifying adopted goals and/or intentions Modifying beliefs (by acting to change world state)
Example: User desires to attend a conference in Europe but lacks sufficient discretionary funds
shorten a previously scheduled trip cancel the planned purchase of a new laptop or apply for a travel grant from the department
Combined Commitment Deliberation
Goal adoption Adopted Goals Candidate Goals ( Desires)
Intention reconsideration Extended agent life-cycle Non-adopted Candidate Goals Execution problems with Adopted Goals
Propose combined commitment deliberation mechanism
Based on agent’s deliberation over its mental states Bounded rationality: as far as the agent believes and
can compute
BDI Control Cycle
identify changes to mental state
decide on response
perform actions
world state changes
commitment deliberation
Mental State Transitions
Current mental state S = (B,GC,GA,I) Omit D since suppose single “satisfy user” desire
Outcome of deliberation is new state S' Possible new transitions:
Expansion adopt additional goal No modification to existing goals or intentions
Revocation drop adopted goal + intention To enable a different goal in the future
Proactive create new candidate goal and adopt it To enable a current candidate goal in the future
Plus standard BGI transitions E.g. drop an intention due to plan failure
observe decide
act
commitmentdeliberation
Goal and Intention Attributes
Goals: User-specified value/utility
Can be time-varying User-specified priority User-specified deadline Estimate cost to achieve
Level of commitment so far For adopted goals
Intentions: Implied value/utility Cost of change
Deliberative effort Loss of utility Delay
Level of commitment Level of effort so far
E.g. estimated % complete Estimated cost to complete Estimated prob. success
Making the Best Decision
S→S' transition as multi-criteria optimization Maximize (minimize) some combination of criteria over S Can be simple or complex
Bounded rationality Simple default strategy, customizable by user
Advice acts as constraints constrained (soft) multi-criteria optimization problem
“Don’t drop any intention > 70% complete” Assistive agent can consult user if no clear best S'
“Should I give up on purchasing a laptop, in order to satisfy your decision to travel to both conferences?”
Learn and refine model of user’s preferences
Example
Candidate goals: c1: “Purchase a laptop”
c2: “Attend AAAI”
Adopted goals and intentions: g1 with intention i1: “Purchase a high-end laptop using
general funds” g2 with intention i2: “Attend AAAI and its workshops,
staying in conference hotel” New candidate goal from user:
c3: “Attend AAMAS” (high priority)
Mental state S = (B, {c1,c2,c3}, {g1,g2}, {i1,i2})
Example (cont.)
CALO finds cannot adopt c3
{g1,g2,g3} resource contention – insufficient general funds
Options include:1. Do not adopt c3 (don’t attend AAMAS)
2. Drop c1 or c2 (laptop purchase or AAAI attendance)
3. Modify g2 to attend only the main AAAI conference But changing i2 incurs a financial penalty
4. Adopt a new candidate goal c4 to apply for a departmental travel grant
Advice prohibits option 2
Example (cont.)
CALO builds optimization problem and solves it Problem constructed and solution method employed both
depend on agent’s nature E.g. ignore % of intention completed No more than 10ms to solve
Finds best is tie between options 3 and 4 Agent’s strategy (based on user guidance) is to consult
user over which to do User instructs CALO to do both options
New mental state S' = (B', {c1,c2,c3,c4}, {g1,g'2,g3,g4}, {i1,i'2})
Overview
CALO: a learning cognitive assistant User delegation of tasks to CALO Delegative BDI agent framework Goal adoption and commitments Summary and research issues
Summary
CALO acts as user’s intelligent assistant Classical BDI framework inadequate Implemented BDI systems lack formal grounding
Proposed delegative BDI agent framework Separate Desires and Goals Separate Candidate and Adopted Goals Incorporate user guidance and preferences Combined commitment deliberation for goal adoption and
intention reconsideration Enables reasoning necessary for an agent such as CALO
Implemented by extending SPARK agent framework
Related Work
BOID framework [Broersen et al] Different types of agents based on B/D/G/I conflict
resolution strategies
BDGICTL logic [Dastani et al] Merging desires into goals
Intention reconsideration [Schut et al] Collaborative problem solving [Leveque and
Cohen; Allen and Ferguson] Social norms and obligations [Dignum et al]
Future Work
Extend goal reasoning to consider resource feasibility (in progress)
Proactive goal anticipation and adoption Collaborative human-CALO problem solving
Beyond (merely) completing user-delegated tasks Multi-CALO coordination and teamwork Learning as part of CALO’s extended life-cycle
More information: http://calo.sri.com/