to Model Social Cognition

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14 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

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Altering the I
CARUS

Architecture

to Model Social Cognition


Pat Langley

Institute for the Study of

Learning and Expertise

Award Period 2/1/12

1/31/15


ONR Cognitive Science and Human
-
Robot Interaction


6.1 Program Review

June 25

28, 2013

Critique of the I
CARUS
Architecture

In previous work (Langley et al., 2009), we have developed
I
CARUS
, an architecture that, despite its accomplishments:


Relies on exhaustive, deductive inference


Emphasizes physical activities over mental ones


Cannot represent or reason about others’ mental states


Has inflexible mechanisms for execution / problem solving

This project aims to address these drawbacks by developing
a radically new version of the architecture.

3

Research Objectives

We aim to develop a unified theory of the human cognitive
architecture that supports:



Representing and reasoning about others’ mental states



Flexible inference and problem solving in this context



Structural learning that supports these processes

The research project’s significance lies in its potential to:



Improve accounts of human reasoning and learning



Support agents/robots that interact effectively with humans

This effort addresses aspects of high
-
level cognition that have
received little attention elsewhere.


During the past year, our team’s accomplishments have included:



Developing new formalisms for:


Beliefs and goals that refer to other agents’ mental states


Concepts and skills that involve relations among mental states


Designing, implementing, and testing an approach to the incremental
abduction of explanations


Adapting and applying this mechanism to:


Understanding domain
-
level plans


Understanding stories in which agents reason about others


Explaining and judging behavior in moral contexts


Reimplementing / improving a flexible framework for problem solving
that incorporates meta
-
level control rules

Together, these support our aims to produce a more complete account
of human cognitive abilities.

Recent Accomplishments

Challenge: Plan Understanding

A basic task that involves reasoning about others' mental states
is
plan understanding
, which we can define as:


Given:

A sequence S of actions agent A is observed to carry out;


Given:

Knowledge about concepts and activities, organized
hierarchically, that are available to agent A;


Infer:

An explanation, E, in proof lattice form, that accounts for
S in terms of A's goals, beliefs, and intentions.

This is analogous to
language understanding

in that analysis
produces a connected account of input.

We distinguish it from
plan recognition

(Goldman et al., 1999),
which assigns observed behavior to some known category.

An Illustrative Example

Consider an action sequence from the Monroe County corpus
(Blaylock & Allen, 2005):

Truck driver
tdriver1

navigates the dump truck
dtruck1

to the
location
brightondump
, where a hazard team
ht2

climbs into
the vehicle. Then
tdriver1

navigates
dtruck1

to the gas station
texaco1
, where
ht2

loads a generator
gen2

into
dtruck1


Given such observations and knowledge about possible goals /
activities, we want to infer the latter to explain events.

In this case, we might conclude the driver is collecting people and
a power source for some mission.

Plan Understanding as Abductive Inference

Our theoretical claims about plan understanding are that it:


Involves inference about the participating agents’
mental states

(beliefs / goals about activities and environment)


Involves the
abductive

generation of
explanations

through the
introduction of default assumptions


Operates in an
incremental

fashion to process observations that
arrive sequentially


Proceeds in a
data
-
driven

manner because understanding arises
from observations about agents’ activities

These four assumptions place constraints on our computational
account of this important process.

A Sample Explanation

get
-
to(ht2, texaco1)



get
-
to(dtruck1, br
-
dump)



drive
-
to(tdriver1, dtruck1, br
-
dump)




at
-
loc(dtruck1, _)




at
-
loc(tdriver1, _)



navigate
-
vehicle(tdriver1, dtruck1, br
-
dump)




person(tdriver1)




vehicle(dtruck1)




can
-
drive(tdriver1, dtruck1)




at
-
loc(dtruck1, br
-
dump)




at
-
loc(tdriver1, br
-
dump)



get
-
in(ht2, dtruck1)




not(non
-
ambulatory(ht2))




person(ht2)



climb
-
in(ht2, dtruck1)




at
-
loc(ht2, br
-
dump)




at
-
loc(dtruck1, br
-
dump)




fit
-
in(ht2, dtruck1)




at
-
loc(ht2, dtruck1)




get
-
to(dtruck1, texaco1)



drive
-
to(tdriver1, dtruck1, texaco1)




at
-
loc(dtruck1, br
-
dump)




at
-
loc(tdriver1, br
-
dump)



navigate
-
vehicle(tdriver1, dtruck1, texaco1)




person(tdriver1)




vehicle(dtruck1)




can
-
drive(tdriver1, dtruck1)




at
-
loc(dtruck1, texaco1)




at
-
loc(tdriver1, texaco1)



get
-
out(ht2, dtruck1)




not(non
-
ambulatory(ht2))




person(ht2)



climb
-
out(ht2, dtruck1)




at
-
loc(ht2, dtruck1)




at
-
loc(dtruck1, texaco1)




at
-
loc(ht2, texaco1)

Representing Plan Knowledge

navigate_vehicle(Driver, Veh, Loc, T_Start, T_End)


at_location(Veh, VLoc, T_1, T_Start),


at_location(Driver, VLoc, T_3, T_Start),


Driver(Driver), vehicle(Veh),


can_drive(Driver, Veh, T_9, T_10),


at_location(Veh, Loc, T_End, T_13),


at_location(Driver, Loc, T_End, T_15),


constraint(before(T_1, T_Start)), constraint(before(T_2, T_Start)),


constraint(before(T_3, T_Start)), constraint(before(T_4, T_Start)),


constraint(inside(T_Start, T_End, T_5, T_6)), constraint(before(T_End, T_14)),


constraint(inside(T_Start, T_End, T_7, T_8)), constraint(before(T_End, T_13)),


constraint(inside(T_Start, T_End, T_9, T_10)), constraint(before(T_End, T_15)),


constraint(inside(T_Start, T_End, T_11, T_12)), constraint(before(T_End, T_16)).

We represent knowledge about activities in a notation similar to
hierarchical task networks. For example:

This formalism separates conditions, effects, and invariants in
terms of temporal constraints on antecedents.

The UMBRA Abduction System



Accepts observations and adds them to working memory



Incrementally extends an explanation by:


-

Finding rules with antecedents that unify with wm elements


-

Tentatively completing each rule instance's missing antecedents


-

Selecting the rule instance R with best evaluation score


-

Adding R’s inferred elements to memory as default assumptions



Continues until no further observations arrive

We have developed UMBRA, an abductive inference system that:

This data
-
driven strategy aims to produce a coherent explanation
in terms of available knowledge.

UMBRA is similar in spirit to AbRA (
Bridewell & Langley, 2011
).

Experiments on Plan Understanding

Experiments with UMBRA on the Monroe corpus show that:


The system can reconstruct much higher
-
level plan structure


Even when only a fraction of agent actions are observed


Incremental abduction is nearly as effective as batch processing

Results on Plan Understanding

Precision and recall for
each problem on ten
‘batch’ runs.

The former is very high
on some tasks but not as
good on others.

Differences are due to
features of problems in
the Monroe domain.

Recall is mediocre for
similar reasons.


Challenge: Social Understanding in Fables

A more challenging task involves reasoning about plans that
take others' mental states in account.

This ability is required to understand Aesop
-
style fables like:

Explanations of such stories include beliefs and goals about
others’ beliefs and goals.

This requires extensions to representations in both working
memory and long
-
term knowledge.

The Snake, the Lion and the Sheep.

The lion is too old to chase
down animals. The lion announces he is sick. The sheep, believing
he is harmless, follows social convention and visits the lion's caves
to pay his respects. The lion kills and devours the sheep. A snake
watches these events and understands the deception that occurred.

Extending Working Memory


belief(fox, has(crow, grapes, 0930, _), 0931, _)


goal(crow, acquire_edible_food(crow, _, _))


belief(snake, belief(lion,


at_location(lion, river, 0900, _), 0902, _), 0902, _)


belief(snake, goal(fox,


trade_food(crow, grapes, fox, grain, 0940, _), 0930, _), 0933, _)


goal(lion, belief(sheep, sick(lion, 0900, 2400), 0945, _), 0900, _)

UMBRA represents agents’ mental states in terms of embedded
structures like:

Elements of this sort provide building blocks for explanations
of scenarios that involve agents reasoning about others.

Extending Knowledge about Activities

announce_falsehood(Actor, Agent2, Content, START, END)


neg(dead(Actor, T1, T2)),


exists(Actor, T3, T4),


belief(Actor, neg(Content), T5, T6),


agent(Actor),


agent(Agent2),


announce_act(Actor, Agent2, Content, T_S, T_END),


belief(Agent2, Content, T_END, T7),


belief(Actor, belief(Agent2, Content, T_END, T8), T_END, T9),


constraint(inside(T_S, T_END, T1, T2)),


constraint(before(T_END, T8)),


constraint(before(T_S, T_END)).

UMRBA also requires planning operators that influence others'
mental states, such as for communicative actions:

These structures, combined with domain knowledge, support
abductive construction of complex social explanations.

A Testbed for Social Understanding




About 60 distinct skills / operators




alternative decompositions




many with overlapping conditions




only ten percent used in any 'correct' fable explanation




about 500 domain
-
level conditions, excluding constraints



About 100 distinct domain
-
level predicates

We have constructed a domain and test scenarios, based largely
on Aesop's fables, with knowledge that includes:

Most of the six scenarios involve plans that depend on one or
more agents reasoning about the mental states of others.

Results on Social Understanding

Nested understanding:

The primary agent interprets another agent's
mental states and/or plan based on observed behavior.

Feeling hungry, a crow travels to a barn and acquires grain by opening a jar. A
snake watches and understands the crow solving her simple problem.


Deeply nested understanding:

The primary agent infers a secondary
agent’s inferences about a third agent's mental states.

A fox, watching the snake watching the crow, imagines what the snake thinks
about the crow's situation.


Inferring
mistakes

in understanding:

The primary agent infers
another agent's mistaken beliefs, why they arise, and the true account.

A lion is proud of his mane. He passes by a river, sees his reflection, and
attacks the ‘other’ lion. An observing snake infers why he takes this action.

We have tested UMBRA on ‘fable’ scenarios that involve different
levels of complexity beyond ‘basic’ plan understanding.

Results on Social Understanding

Reasoning about
opportunism

in understanding:

The primary agent
understands how another agent capitalizes upon another's false beliefs.

A hungry crow in possession of some sour grapes trades them to a fox, who
assumes they are sweet, in return for delicious grain. A watching snake
explains the interaction.


Reasoning about
deception

in understanding:

The primary agent
infers than another agent deliberately engenders false beliefs in a third
agent in order to achieve some goal.

A

lion is too old to chase down animals. The lion announces he is sick. The
sheep, believing he is harmless, follows social convention and visits the lion's
caves to pay his respects. The lion kills and devours the sheep. A snake who
watches these events and understands the deception that occurred.

UMBRA constructs the desired explanations for each scenario, some
of which involve deeply embedded mental models.

Complete Structure of a Fable Explanation

Green = condition

Yellow = effect

Orange = invariant

Blue = constraint

Diamond = task / skill

Green = condition

Yellow = effect

Orange = invariant

Blue = constraint

Diamond = task / skill

Portion of a Fable Explanation

Green = condition

Yellow = effect

Orange = invariant

Blue = constraint

Diamond = task / skill

One Element of a Fable Explanation

Green = condition

Yellow = effect

Orange = invariant

Blue = constraint

Diamond = task / skill

Challenge: Moral Judgement

An even more challenging cognitive task involves complex
moral judgement, which we can specify as:


Given:

A sequence S of observed actions, including the agent(s)
A who performed them;


Given:

Knowledge about these and related events, including their
relation to moral concepts;


Infer:
An
explanation

E that accounts for S in terms of this
knowledge and A’s beliefs, goals, and intentions; and


Infer:
A moral
evaluation

of S that takes into account the
explanation E.


This task combines plan understanding with evaluation in terms
of moral concepts.

Claims about Moral Judgement

We maintain that
complex

moral judgement is a form of social
plan understanding in that it:


Focuses on the
mental states

of agents who interact in a
given scenario;


Depends on rules that abstract away from domain
-
specific
details and focus on
relations

among mental states;


Involves the linking of rule instances into some connected
explanation

of observed behavior.

However, the process also relies on calculating
numeric values

on elements that reflect evaluations of behavior.

A Sample Moral Explanation

Consider a scenario in which one agent (John) causes another
(Kelly) to feel pain by shoving her.

We might infer that John carried out this action deliberately so
that Kelly would experience distress.

Evaluations of Moral Explanations

We plan to extend UMBRA to support the evaluation of moral
explanations by:

We also maintain that top
-
down influences account for the effect
of mitigating factors on judgement scores.



Adding numeric annotations to long
-
term knowledge structures:




A default weight for each conceptual predicate




An upward factor for each rule's antecedent




A downward factor for each rule's antecedent



Calculating an evaluation for each element in an explanation by:




Multiplying the sum of upward factors by the default value and


propagating the result upward to the root(s)




Multiplying downward factors by the accrued values at root(s)


Utilizes means
-
ends analysis


Carries out depth
-
first search


Interleaves tightly with skill execution


Cannot reason about others’ mental states

The current I
CARUS

architecture incorporates a distinct module
for problem solving that:

These features do not reflect the character of human problem
solving, which is far more flexible.

Our new framework aims to support such flexibility by using
meta
-
level knowledge
.

Problem Solving in I
CARUS


Search strategies (
depth first
,
breadth
-
first
,
iterative sampling
)


Intention selection strategies (
means ends
,
forward search
)


Intention application strategies (
eager
,
delayed commitment
)


Failure conditions (
depth limited
, effort limited,
loops
)


Solution conditions (
single
, multiple, all)

We have redsigned and reimplemented our meta
-
level approach
to problem solving to support different:

These behaviors are produced by differences among meta
-
level,
domain
-
independent control rules associated with five modules.

Soar (Laird, 2012) takes a similar but finer
-
grained approach; our
framework is closer to that in Prodigy (Minton, 1988).

Flexible Problem Solving

Organization of Problem Solving

Problem solving
occurs in cycles,
with meta
-
level
rules determining
behavior at each
successive stage.

Meta
-
level rules
determine the
system’s behavior
for each stage.

Problem Decompositions

Problems

play the
central organizing
structure in our
framework.

Down

subproblems
have the same state
as their parents.

Right

subproblems
have the same goals
as their parents.


This organization is
the same as that in
means
-
ends problem
solving, but we use
it to support very
different strategies.

Plans for Future Research


Extend UMBRA to support
belief revision

when it decides its
default assumptions are faulty


Augment the meta
-
level problem solver to support
execution

of
plans in the environment


Integrate UMBRA’s inference mechanism with our approach to
flexible problem solving


Introduce mechanisms for
learning

structures from explanations


Carry our experiments to demonstrate these extensions’ benefits

Although we have made substantial progress toward the project
goals, we still need to:

The resulting architecture should offer a more complete account
of high
-
level cognition in humans.

Summary Remarks



Represents mental states in terms of embedded beliefs / goals



Incorporates an incremental approach to abductive inference



Combines these to support plan understanding



Basic explanations of observed physical activities



Explanations that involve agents reasoning about other agents



Moral judgements that include inferences about agent intentions



Uses meta
-
level control to support flexible problem solving

In this talk, I presented elements of a new cognitive architecture
that addresses limitations of I
CARUS

by:

When integrated, these should give us a new version of I
CARUS

that has substantially greater breadth and flexibility.

Publications and Presentations

Langley, P. (2012). The cognitive systems paradigm.
Advances in Cognitive Systems
,
1
, 3
-
13.

Langley, P. (2012). Intelligent behavior in humans and machines.
Advances in Cognitive
Systems
,
2
, 3
-
12.

MacLellan, C., Langley, P., & Walker, C. (2012). A generative theory of problem solving.
Poster Collection

/
First Annual Conference on Advances in Cognitive Systems
, 1
-
18.

Meadows, B., Langley, P., & Emery, M. (in press). Seeing beyond shadows: Incremental
abductive explanation for plan understanding. Proceedings of the
AAAI
-
2013 Workshop
on Plan, Activity, and Intent Recognition.

Liu, L., Langley, P., & Meadows, B. (in press). A computational account of complex moral
judgement.
Proceedings of the

Annual Conference

of the

International Association for
Computing and Philosophy.

The Cognitive Systems Paradigm. Presented at AAAI Fall Symposium on Advances in
Cognitive Systems, Arlington, VA, November, 2011.

Intelligent Behavior in Humans and Machines. Presented at First Annual Conference on
Advances in Cognitive Systems, Palo Alto, CA, December, 2012.

Cooperative Development


Commitments to hierarchical concepts / skills borrowed from
initial I
CARUS

architecture developed under ONR funding


Representation of mental states developed jointly with ONR
MURI project at CMU


Ideas on abductive inference co
-
developed with W. Bridewell
in ONR MURI work at Stanford

Our research on this project has benefited from results produced
on a number of other efforts:

These efforts have let us make more rapid progress than would
have been possible otherwise.

Transition Plan


Virtual medical assistants that interact with field medics to help
them provide emergency care


Cognitive robots that interact with Navy personnel dealing
with shipboard problems (e.g., fighting fires)

Our research on computational social cognition has clear uses in
virtual agents and human
-
robot interaction.

In the longer term, we hope to transition our results to applied
settings like:

We hope to take advantage of existing relationships with NRL
researchers to increase the chances of successful transitions.

Project Budget

The research project’s budget, by federal fiscal year, is:




FY2012: $118K



FY2013: $179K



FY2014: $182K



FY2014: $ 60K


No DURIP were awarded in relation to this project.


End of Presentation