The Role of Cognition in Cognitive Systemsjjb/ftp/Bryson-CogMod-Manchester10.pdf · Intelligence •What matters is expressing the right behavior at the right time: action selection

The Role of Cognition in Cognitive Systems

From Robots to Primatology Joanna J. Bryson

Artificial Models of Natural IntelligenceUniversity of Bath

Institute of Cognitive and Evolutionary Anthropology University of Oxford

http://www.cs.bath.ac.uk/ai/AmonI.html

Outline

• Introduction to Intelligence & Cognition

• Where do you put it in a Cognitive System? Behavior Oriented Design

• Primates & Cognition

Intelligence

• What matters is expressing the right behavior at the right time: action selection.

Tony J. Prescott, Joanna J. Bryson, and Anil K. Seth, “Introduction. Modelling Natural Action Selection”, Philosophical Transactions of the Royal Society -- B, Biology 362(1485): 1521–1529, Sept 2007.

Intelligence


• Conventional AI planning searches for an action sequence, requires set of primitives.


Intelligence


• Conventional AI planning searches for an action sequence, requires set of primitives.

• Learning searches for the right parameter values, requires primitives and parameters.

• Evolution and development are learning.


Combinatorics• If . . .

– an agent knows 100 actions (e.g. eat, drink, sleep, step, turn, lift, grasp, poke, flip...), and – it has a goal (e.g. go to Madagascar)

• Then . . .

– Finding a one-step plan may take 100 acts. – A two-step plan may take 1002 (10,000). – For unknown number of steps, may search forever, missing critical steps or sequence.

Intelligence & Design

Joanna J. Bryson and Lynn Andrea Stein, “Modularity and Design in Reactive Intelligence”, in The Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), Seattle WA, pp. 1115–1120, Morgan Kaufmann, 2001.

Intelligence & Design• Combinatorics is the problem, search is the only

solution.



solution.

• The task of intelligence is to focus search.

• Called bias (learning) or constraint (planning).

• Most behavior has no or little real-time search.



solution.




• For natural intelligence, most focus evolves.



solution.




• For natural intelligence, most focus evolves.

• For artificial intelligence, most focus designed.Joanna J. Bryson and Lynn Andrea Stein, “Modularity and Design in Reactive Intelligence”, in The Seventeenth International Joint Conference on Artificial Intelligence (IJCAI), Seattle WA, pp. 1115–1120, Morgan Kaufmann, 2001.

Cognition

Cognition

Definition:

Cognition is on-line (real-time) search.

Cognition

Definition:

Cognition is on-line (real-time) search.

Consequence:

Cognition is bad.

Cognition

Cognition

• Why is cognition / individual search bad?

• Slow

• Uncertain

Cognition

• Why is cognition / individual search bad?

• Slow

• Uncertain

• Unpopular in most species.

• Plants

• Protozoa

Only think when you don’t know what’s going on

• Cognition is costly.

• Time, errors metabolism.

• Value of investment can be estimated from own experience or mother’s (maternal effects).

Deary et al. (2004)(Schaie et al. 2004; Kotrschal

& Taborsky in prep.)Joanna J. Bryson, “Age-Related Inhibition and Learning Effects: Evidence from Transitive Performance”, in Proceedings of the 31st Annual Meeting of the Cognitive Science Society (CogSci 2009) pp. 3040–3045.

Outline




Architecture

• Where do you put the cognition?

• Really: How do you bias / constrain / focus cognition (learning, search) so it works?

Behavior Oriented Design

• All search (learning, planning) is done within modules with specialised representations.

• Specialized representations promote reliability of search; also determine decomposition.

• Modules provide perception, action, memory. Arbitration via hierarchical dynamic plans.

• Iterative / agile test & development cycle.

(Bryson 2001, 2003)

BOD Action SelectionParallel-rooted, Ordered, Slip-stack Hierarchical (POSH) action selection:

• Some things need to be checked at all times: drive collection.

• Some things only need considering in particular context: competences.

• Some things reliably follow from others: action patterns.

BOD Robot Example

• Behaviour Library — per platform.

• POSH plan — per “species” / goal set.

• Memory — per individual.

(ATAL 1997, PhD 2001)

Joanna J. Bryson “The Behavior-Oriented Design of Modular Agent Intelligence”, Agent Technologies, Infrastructures, Tools, and Applications for e-Services, R. Kowalszyk, J. P. Müller,H. Tianfield and R. Unland, eds., pp. 61–76, Springer, 2003.

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DP-Map

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untried near neighbor?, untried far neighbor? ��

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Selection

DP-Landx,yin-dirout-dir

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E-Memory

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life (D)

talk [1/120 Hz]

(worth talking�)

speak

sense (C) [7 Hz]

bump (bumped�) yelp reg bump back off clear bump lose direction

look compound sense

walk (C)

halt (has direction�)

(move view ’blocked)

lose direction

start (has direction⊥) pick open dir

continue move narrow (move view ’clear) correct dir

wait snore sleep

walk (C)

halt (has direction�)

(move view ’blocked)

lose direction

cogitate route (C)

enter dp (in dp ⊥)

(entered dp⊥)

lose direction greet dp

leave dp (in dp �)

(entered dp�)

dismiss dp

pick direction (C)

look up

(untried near neighbor

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keep going

(continue untried�)

pick previous direction

desperate look up

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start (has direction⊥) ask directions

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Statistical Testing of BOD Action Selection

Tests performed in Tyrellʼs (1993) “Simulated Environment”

0 1 2 3 4 5 6 7 8 92

4

6

8

10

12

14

(Sparse)Std (Sparse)Var1 (Sparse)Var2 (Sparse)Var3

Fitn

ess

life (D)

flee (C) (sniff predator t)

freeze (see predator t) (covered t) (hawk t) hold still

run away (see predator t) pick safe dir go fast

look observe predator

mate (C) (sniff mate t)

inseminate (courted mate here t) copulate

court (mate here t) strut

pursue pick dir mate go

triangulate (getting lost t) pick dir home go

home 1::5 (late t) (at home⊥) pick dir home go

check 1::5 look around

exploit (C) (day time t)

use resource (needed res avail t) exploit resource

leave pick dir go

sleep at home (at home t) (day time⊥) sleep

N NE E SE S SW W NW

UTReproduce

1.4

T U

Move ActionsMate

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No Denin Sq

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= positive activation

= small positive activation

= zero activation

= large positive activation(1.0)

Joanna J. Bryson, “Hierarchy and Sequence vs. Full Parallelism in Action Selection”, Simulation of Adaptive Behavior 6, pp. 147–156. 2000.

BOD Monkey Science

Joanna J. Bryson and Jonathan C. S. Leong “Primate Errors in Transitive ‘Inference’” Animal Cognition, 10(1):1–15, January 2007.

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• If each agent has a 1% chance of discovering a skill (e.g. making cheese) in its lifetime and there are 4000 agents, probably some agents will know the skill.

• If it is easier to learn the skill from a knowledgeable agent than by discovery, then selective pressure for culture.

• Inclusive fitness c < b × r (Hamilton 1964; West et al 2007).

Combinatorics vs Culture

BOD Experiments in Social Learning in VR

Mark A. Wood and Joanna J. Bryson, “Skill Acquisition Through Program-Level Imitation in a Real-Time Domain”, IEEE Transactions on Systems, Man and Cybernetics Part B—Cybernetics, 37(2):272–285, April 2007.

Basic Result: Still intractable without an enormous amount of

prior information.

Extension of Roy 1999 (PhD) to realtime planning.

Fortunately, Priors Easy to Insert with BOD

Samuel J. Partington and Joanna J. Bryson, “The Behavior Oriented Design of an Unreal Tournament Character”, The Fifth International Working Conference on Intelligent Virtual Agents, pp. 466–477, Springer, 2005.Cyril Brom, Jakub Gemrot, Michal Bída, Ondrej Burkert, Sam J. Partington and Joanna J. Bryson, “POSH Tools for Game Agent Development by Students and Non-Programmers”, in The Nineth International Computer Games Conference: AI, Mobile, Educational and Serious Games, pp. 126–133, 2006.

IDEs for Dynamic

Plans

• Advanced BOD Environment.

• Ubiquitous robotics requires AI (or servicing AI) by graduates with second class honours.

Joanna J. Bryson, Tristan J. Caulfield and Jan Drugowitsch, “Integrating Life-Like Action Selection into Cycle-Based Agent Simulation Environments”, in Proceedings of Agent 2005:Generative Social Processes, Models, and Mechanisms. pp. 67–81, Argonne National Laboratory 2006.

IDEs for Dynamic

Plans

• Advanced BOD Environment.

• Ubiquitous robotics requires AI (or servicing AI) by graduates with second class honours.

This work was & is again being funded by

aerospace.

Joanna J. Bryson, Tristan J. Caulfield and Jan Drugowitsch, “Integrating Life-Like Action Selection into Cycle-Based Agent Simulation Environments”, in Proceedings of Agent 2005:Generative Social Processes, Models, and Mechanisms. pp. 67–81, Argonne National Laboratory 2006.

Outline




Why BOD Works• Modularity: problem spaces, combat

combinatorics, allow locally-optimal representations.

• Should use ordinary (OO) code (arbitrarily powerful but also access to primitives.)

• Hierarchical action selection for arbitration.

• Dedicated, high-frequency goal / attention switching, keeps hierarchical AS responsive.

• Agile development, refactoring (Beck 2000).

Subsumption (Brooks 1986)

Subsumption (Brooks 1986)• Emphasis on

sensing to action (via Augmented FSM).



• Very complicated, distributed arbitration.




• No learning.

Sept 1

1 9 9 3

Sept 1

1 9 9 4Sept 1

1 9 9 5

Sept 1

1 9 9 6

Sept 1

1 9 9 7

Syst em sof t ware ( 0 t h) Syst em sof t ware ( commercial processor)

Periperhal Mot ion

Saccades

VOR

Smoot h pursuit

Vergence

based st ereo

Ullman-esque

visual rout ines

Face pop-out s Face remembering Face recognit ion

Gest ure recognit ion Facial gest ure recog. Body mot ion recog.

Own hand t racking

Physical schema

based obj. recog.

Bring hands

midline

Hand

linking

Grasping,

& t ransf er

Specif ic obj. recog. Generic object recog.

Body-based met aphors

DOF reduct ion

( specif ic coords)

DOF reduct ion

( generic coords)Bat t ing st at ic

ob ject s

Head/ eye coord

Body st abilit y ,

leaning, rest ing

Head/ body / ey e/ coord

Body+arm reaching Body mimicry

Manipulat ion t urn t aking

Sound localizat ion

Sound/ mot ion correl Human voice ext ract ion

Sound-based manip. Voice/ f ace assoc

Voice t urn t aking

Prot o language

Visual imagery Symbolizat ion

Imaginat ionMent al rehearsal

Mult iple-draf t s emergence

Tone ident if icat ion

“Building Brains

for Bodies”, Brooks & Stein (1993), MIT AI lab tech report 1439.

Sept 1

1 9 9 3

Sept 1

1 9 9 4Sept 1

1 9 9 5

Sept 1

1 9 9 6

Sept 1

1 9 9 7


Periperhal Mot ion

Saccades

VOR

Smoot h pursuit

Vergence

based st ereo

Ullman-esque

visual rout ines



Own hand t racking

Physical schema

based obj. recog.

Bring hands

midline

Hand

linking

Grasping,

& t ransf er



DOF reduct ion

( specif ic coords)

DOF reduct ion


ob ject s

Head/ eye coord

Body st abilit y ,

leaning, rest ing




Sound localizat ion



Voice t urn t aking

Prot o language





“Building Brains


Sept 1

1 9 9 3

Sept 1

1 9 9 4Sept 1

1 9 9 5

Sept 1

1 9 9 6

Sept 1

1 9 9 7


Periperhal Mot ion

Saccades

VOR

Smoot h pursuit

Vergence

based st ereo

Ullman-esque

visual rout ines



Own hand t racking

Physical schema

based obj. recog.

Bring hands

midline

Hand

linking

Grasping,

& t ransf er



DOF reduct ion

( specif ic coords)

DOF reduct ion


ob ject s

Head/ eye coord

Body st abilit y ,

leaning, rest ing




Sound localizat ion



Voice t urn t aking

Prot o language





“Building Brains


it is nor hand, nor foot, nor arm, nor face, nor any other

part belonging to a man.

What’s Consciousness?

Glenn Matsumura, Wired 2007

SG5-UT Robotic Arm

Tad McGeer's passive dynamic walker

Chuck Rosenbergʼs IT, 1997

Dennett (2008)“Contents arise, get revised, contribute to... the modulation of behavior, and in the process leave their traces in memory...”

“Only [commonality is] the historical property of having won a temporally local competition with sufficient decisiveness... to enable recollection...”

Dennett (2008)“Contents arise, get revised, contribute to... the modulation of behavior, and in the process leave their traces in memory...”

“Only [commonality is] the historical property of having won a temporally local competition with sufficient decisiveness... to enable recollection...”

• Characteristics:

1. Selection from concurrent options.

2. Indicated by episodic memory.

Function-Based Theory

• Consciousness is holding one stimulus in mind while searching options primed by it for a better response.

• Only triggered when next action isn’t obvious (reflexive or trained).

• Side effect: special types of learning.

• Side effect: long reaction times. Focus attention longer when less certain.

Joanna J. Bryson, “Crude, Cheesy, Second-Rate Consciousness”, The Second AISB Symposium Computing and Philosophy, Mark Bishop (ed), pp. 10–15, Edinburgh UK, April 2009.

Monkeys Learning New Rewards (or not)

• Monkeys that learn chained pairs of values (A>B; B>C; C>D; D>E; E>F) normally are faster at assessing stimuli the further they are on the chain (B>E faster than B>D).

• Elderly monkeys are always fast.

• Elderly monkeys also don’t learn when you change the reward scheme -- not aware?

Herb Terrace, Columbia, NY

Joanna J. Bryson, “Age-Related Inhibition and Learning Effects: Evidence from Transitive Performance”, in Proceedings of the 31st Annual Meeting of the Cognitive Science Society (CogSci 2009) pp. 3040–3045.

What’s Consciousness?

• A module for learning new action selection.

• Triggered by uncertainty.

• Detectable due to episodic memories & reaction time.

• A finite resource constantly directed towards what seems most surprising.

“If the best the roboticists can hope for is the creation of some

crude, cheesy, second-rate artificial consciousness, they still win.”

D. C. Dennett (1994), “The Practical Requirements for Making a Conscious Robot”,

Philosophical Transactions: Physical Sciences and Engineering, 349 p. 137 (133-146).

Outline




• Conclusions

Cognitive Robots

Bad News:

Cognitive Robots

Bad News:

Cognition doesn’t require robots (only rich, dynamic, real-time environments).

Cognitive Robots

Bad News:


Robots don’t require cognition.

Cognitive Robots

Bad News:



Good News:

Cognitive Robots

Bad News:



Good News:

Cognitive robots are still pretty interesting.

What I Learned from Robots

What I Learned from Robots1. Perception is hard -- which explains the brain.

• Lead to specialized representations encapsulated in modules; my method of behavior-module decomposition.

What I Learned from Robots1. Perception is hard -- which explains the brain.

• Lead to specialized representations encapsulated in modules; my method of behavior-module decomposition.

2. Discrete action selection is compatible with continuous acting, provided the primitive `acts’ alter ongoing behaviour supported by modules.

• e.g. motor act sends target velocity, not vector;

• multiple || devices/modules e.g. speech, motion.

The BrainHigher mammals separate sense & action (Central Sulcus).

Chance for Cognition?(images: Carlson)

When Your Robot Must Think...

• Modularity: problem spaces, combat combinatorics, allow locally-optimal representations.

• Hierarchical action selection for real-time arbitration between modules.

• Dedicated, high-frequency goal / attention switching, compensates for hierarchical AS.

• Agile development, refactoring (Beck 2000).

Thanks!

Mark Wood Jan Drugowitsch

Sam Partington

Tristan Caulfield

Cyril Brom (et al)

Jon Leong

The Role of Cognition in Cognitive Systems

From Robots to Primatology Joanna J. Bryson

Artificial Models of Natural IntelligenceUniversity of Bath

Institute of Cognitive and Evolutionary Anthropology University of Oxford

http://www.cs.bath.ac.uk/ai/AmonI.html

Functionalist Assumption: All we care about is producing intelligent behaviour.

• Physical Symbol System Hypothesis (Newell & Simon 1963); Qualia, Chalmers “hard problem” (1995).

• Thinking, consciousness as epiphenomena (Churchland 1988, Brooks & Stein 1993).

Science: We’ll build it to see if we need it.

Action Selection

Evolving Culture• Old theories of limits:

altruism, rate of environmental change.

• Concurrency can accelerate behaviour change (Bryson 2008).

• Čače & Bryson (2005, 2007) show altruistic communication about food is selected for due to niche creation.

Ivana Čače and Joanna J. Bryson, “Agent Based Modelling of Communication Costs: Why Information can be Free”, in Emergence and Evolution of Linguistic Communication C. Lyon, C.L Nehaniv and A. Cangelosi, eds., pp. 305–322, Springer 2007.

BOD Development Cycle1. Initial decomposition ⇒ specification.

2. Scale the system.

i. Code one behavior and/or plan.

ii. Test and debug code (test earlier plans).

iii. Simplify the design.

3. Revise the specification.







1. Specify (high-level) what the agent will do.

2. Describe activities as sequences of actions. competences and action patterns

3. Identify sensory and action primitives from these sequences.

4. Identify the state necessary to enable the primitives, cluster primitives by shared state. behavior modules

5. Identify and prioritize goals / drives. drive collection

6. Select a first (next) behavior to implement.













Simplify the Design

Use the simplest representations.

• Plans:

• primitives, action patterns, competences.

• drives only if need to always check.

• Behavior modules / memory:

• none, deictic, specialized, general.

(Bryson, AgeS 2003)

Simplify the DesignTrade off representations: plans vs. behaviors

• Use simplest plan structure unless redundancy (split primitives for sequence, add variable state in modules).

• If competences too complicated, introduce primitives or create more hierarchy.

• Split large behaviors, use plans to unify.

• All variable state in modules (deictic).(Bryson, AgeS 2003)

References

References• Cyril Brom and Joanna J. Bryson, “Action Selection for

Intelligent Systems”, white paper for euCognition, 7 August 2006.



• Joanna J. Bryson, “Cross-Paradigm Analysis of Autonomous Agent Architecture”, Journal of Experimental and Theoretical Artificial Intelligence 12(2):165-190, 2000.



• Joanna J. Bryson, “Cross-Paradigm Analysis of Autonomous Agent Architecture”, Journal of Experimental and Theoretical Artificial Intelligence 12(2):165-190, 2000.

• Joanna J. Bryson and Lynn Andrea Stein, “Architectures and Idioms: Making Progress in Agent Design”, The Seventh International Workshop on Agent Theories, Architectures and Languages (ATAL), Boston, 2000.

“History as Evolution” Hypothesis

Joanna J. Bryson, “Cross-Paradigm Analysis of Autonomous Agent Architecture”, Journal of Experimental and Theoretical Artificial Intelligence 12(2):165-190, 2000.


• If an architecture is around for a while, and it changes, the change was probably selected, adaptive.



• If an architecture is around for a while, and it changes, the change was probably selected, adaptive.

• This is particularly likely if the change goes against the stated theories of the architectureʼs makers.


“History as Evolution” Hypothesis & Correlary

“History as Evolution” Hypothesis & Correlary• If an architecture is around for a while,

and it changes, the change was probably selected, adaptive.



• If similar features occur in a lot of architectures with different phylogenies, those features are probably adaptive.



• If similar features occur in a lot of architectures with different phylogenies, those features are probably adaptive.

• If you want to make a contribution to a field, describe your best innovations in terms of well-known systems.

Productions

• From sensing to action (c.f. Skinner; conditioning; Witkowski 2007.)

• These work -- basic component of intelligence.

• The problem is choice (search).

• Requires an arbitration mechanism.

Production-Based Architectures

• Expert Systems: allow choice of policies, e.g. recency, utility, random.

• SOAR: problem spaces (from GPS), impasses, chunk learning.

• ACT-R: (Bayesian) utility, problem spaces (reluctantly, from SOAR/GPS.)

Soar

• Productions operate on predicate database.

• If conflict, declare impasse, reason (search).

• Remember resolution: chunk

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• “Evolution of Soar” is my favorite paper (Laird & Rosenbloom 1996)

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• Admits problems!

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• Admits problems!

• Not enough applications for human-like AI

Architecture Lessons(from CMU)

• An architecture needs:

• action from perception, and

• further structure to combat combinatorics.

• Dealing with time is hard.

ACT-R

• Learns (& executes) productions.

• For arbitration, rely on (Bayesian probabalistic) utility.

• Call it implicit knowledge.

ACT-R Research Programme• Replicate lots of

Cognitive Science results.

• See if the brain does what you think it needs to.

• Win Rumelhart Prize (John Anderson, 2000).

Retrieval Buffer(VLPFC)

Goal Buffer(DLPFC)

Manual Motor(Motor)

Intentional Module(not identified)

External World

Matching (Striatum)

Execution (Thalamus)

Selection (Pallidum)

Pro

du

cti

on

s(B

asalG

an

glia)

Declarative Module(Temporal / Hippocampus)

Visual Buffer(Parietal)

Visual Module(Occipital/Parietal)

Manual Module(Motor/Cerebellum)

Architecture Lessons(from CMU)

• Architectures need productions and problem spaces.

• Real-time is hard.

• Being easy to use can be a win.

Spreading Activation Networks

• “Maes Nets” (Adaptive Neural Arch.; Maes 1989)

• Activation spreads from senses and from goals through net of actions.

• Highest activated



• Sound good:

• easy

• brain-like (priming, action potential).

• Still influential (Franklin 2000, Shanahan 2006).


• Sound good:

• easy

• brain-like (priming, action potential).

• Still influential (Franklin 2000, Shanahan 2006).

• Canʼt do full action selection:

• Donʼt scale; donʼt converge on comsumatory acts (Tyrrell 1993).

Tyrrell (1993)

Extended Rosenblatt and Payton Free-Flow HierarchyN NE E SE S SW W NW

UTReproduce

1.4

T U

Move ActionsMate

-0.08

Court

P. Mate Rand. Dir P. Den R. Den All Dirs

Clean Leavethis Sq

CleanSleep

Mate Court

ApproachMate

Explore For Mates

Explore

Sleep

ApproachP. Den

Approach R. Den

Sleepin Den Clean

Keep

DirtinessLow HealthNight Proxfrom DenDistance

-0.10

-0.05

-0.01

-0.05 -0.05-0.15

Courted Mate in Sq

Mate in SqReceptive

No Denin Sq

Den in Sq

No Den in Sq

in SqDen

-0.02

-0.02-0.25

-0.30-0.04

= small negative activation

= positive activation

= small positive activation

= zero activation

= large positive activation(1.0)

Subsumption (Brooks 1986)









• No learning.




• No learning.

• Worked.

Architecture Lessons(Subsumption)


• Action from perception can provide the further structure -- modules (behaviors).

• Modules also support iterative development / continuous integration.




• Real time should be a core organizing principle -- start in the real world.




• Real time should be a core organizing principle -- start in the real world.

• Good ideas can carry bad ideas a long way (no learning, hard action selection).

Architecture Lesson?• Goals ordering

needs to be flexible.

Architecture Lesson?• Goals ordering

needs to be flexible.

• Maybe spreading activation is good for this.

SA: Layers vs. Behaviours• Relationship not

evident except in development!









Layered or Hybrid Architectures

1. Incorporate behaviors/modules (action from sensing) as “smart” primitives.

2. Use hierarchical dynamic plans for behavior sequencing.

3. (Allegedly) some have automated planner to make plans for layer 2.

• Examples: Firby/RAPS/3T (ʻ97); PRS (1992-2000); Hexmoore ʻ95; Gat ʻ91-98

Belief, Desires, Intentions (BDI)• Beliefs:

Predicates

• Desires: goals & related dynamic plans

• Intentions:current goal

Procedural Reasoning System

Procedural Reasoning System• BDI


• And reactive (responds to emergencies by changing intentions.)


• And reactive (responds to emergencies by changing intentions.)

• Er... once or twice (Bryson ATAL 2000).

Architecture Lessons

• Structured dynamic plans make it easier to get your robot to do complicated stuff.

• Automated planning (or for Soar, chunking/learning) is seldom actually used.

• To facilitate that automated planning, modularity is often compromised.

Soar as a 3LA• J. Laird & P.

Rosenbloom, “The Evolution of the Soar Cognitive Architecture”, Mind Matters, D. Steier and T. Mitchell eds., 1996.

CogAff

• Reflection on Top.

• Sense & Action separated!

• (Davis & Sloman 1995)



• Hierarchy in AS; Goal Swapping (Alarms).

• (Sloman 2000)

CogAff



• Hierarchy in AS, Goal Swapping (now reactive).

• Current Web

CogAff

Architecture Lessons (CogAff)


• Maybe you don’t really want productions as your basic representation -- you may want to come between a sense and an act sometimes.


• Maybe you don’t really want productions as your basic representation -- you may want to come between a sense and an act sometimes.

• Aaron Sloman thinks about a lot more human cognitive traits than I do.

Documents

The Role of Cognition in Cognitive Systemsjjb/ftp/Bryson-CogMod-Manchester10.pdf · Intelligence •What matters is expressing the right behavior at the right time: action selection