A Cognitive Framework for Delegation to an Assistive User Agent

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Feb 23, 2014 (3 years and 3 months ago)

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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


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 tasks

in 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


B
eliefs = informational attitudes about the world


D
esires = motivational attitudes on what to do


I
ntentions = 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

G
A

Belief

B
user

(do assigned tasks)

user

B
agent

Desire

Goal

D
user

D
agent

G
user

G
C
agent

+

+

+

alignment

delegation

refinement

decision

making

goal adoption

Candidate Goals

Adopted Goals

satisfy all
tasks

Delegative BDI Agent Architecture

user

failure

conflicts

revision

advice

A
E

A
G

agent

G
C

G
A

I

execute

B

sub
-
goaling

B

D

G

Candidate Goals

Adopted Goals

Intentions

Goal Advice

Execution 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:

G
A

must be mutually consistent


Coherence:

G
A

must be mutually consistent
relative to the current beliefs B


Feasibility:

G
A

must be mutually satisfiable
relative to current intentions I and available plans


Includes resource feasibility


Reasonableness:

G
A

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,G
C
,G
A
,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

commitment

deliberation

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:


c
1
: “Purchase a laptop”


c
2
: “Attend AAAI”


Adopted goals and intentions:


g
1

with intention i
1
: “Purchase a high
-
end laptop using
general funds”


g
2

with intention i
2
: “Attend AAAI and its workshops,
staying in conference hotel”


New candidate goal from user:


c
3
: “Attend AAMAS” (high priority)


Mental state S = (B, {c
1
,c
2
,c
3
}, {g
1
,g
2
}, {i
1
,i
2
})

Example (cont.)


CALO finds cannot adopt c
3


{g
1
,g
2
,g
3
} resource contention


insufficient general funds


Options include:

1.
Do not adopt c
3

(don’t attend AAMAS)

2.
Drop c
1

or c
2

(laptop purchase or AAAI attendance)

3.
Modify g
2

to attend only the main AAAI conference


But changing i
2

incurs a financial penalty

4.
Adopt a new candidate goal c
4

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
'
, {c
1
,c
2
,c
3
,c
4
}, {g
1
,g
'
2
,g
3
,g
4
}, {i
1
,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


BDGI
CTL

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/