인지기반 지능형 에이전트 설계 : 인식Associative computer: a hybrid connectionistic production sys-tem

Action Editor : John Barnden발제 : 최 봉환 , 04/07, 2009

Outline

• Introduce Associative computer= "a connectionistic hybrid production system"– relies : distributed representation– using : associative memory– action : production system– contribution : learn from experience

• Explain about "Associative computer"– Visual representation of state– Associative memory for state transition– Permutation associative memroy– Problem space

• Demonstrated by empirical experiments in block world

– what is block world

Motivated from Biology : Neural assembly theory

• bridge between the structures found in the nervous system

– In high level cognition such as problem solving – An assembly of neurons

• act as closed system, represent a complex object• activation : some entire ( Hebb, 1958; Palm, 1993 )

• Associative memory– Neural net model + assembly concept ( Palm, 1982 )– A group of inter connected neurons = Hebbian Network

• store patterns new pattern presented a pattern is formed which closely resembles

• The pump of thought model – Theoretical assembly model (Braitenberg, 1973,1984;

Palm, 1982)– How thoughts represented by assemblies

• can be propagated and changed by the brain– The transformation of thoughts through a sequence of

assemblies• describe process of human problem solving (Braitenberg, 1978; Palm,

1982)

Motivated fromPsychology : Mental representation theory

• Thoughts = Description of complex objects– Complex objects : structured and formed by different

fragments• can be represented by categories (Smith, 1995). • categorical representation : how to deal with similarity between ob-

jects• Complex Object description

– verbal : prototypical features– visual (picture) : detailed shape representation– by binary pictograms : size + orientation (Feldman,

1985). • Similarity = the amount of shared area

(Biederman & Ju, 1988; Kurbat, Smith, & Medin, 1994; Smith & Slo-man, 1994).

• items = ( vectors or vector parts ) <> symbols (Anderson, 1995a; Ga¨rdenfors, 2000; McClelland & Rumelhart, 1985; Wichert, 2000, 2001).

Motivated from Computer science : Production system

• Production systems = composed of productions– production = if–then rules– One of the most successful models of human problem

solving• (Anderson, 1983; Klahr & Waterman, 1986; Newell & Simon, 1972;

Newell, 1990)• how to form a sequence of actions which lead to a goal

(Newell, 1990; Winston, 1992). • Memory components

– Long-term memory : complete set of productions• precondition = triggered by specific combinations of symbols

– Short-term memory : Problem-space • "state" = human thought or situation• computation (action) = stepwise transformation

• Searching : backtracking + avoiding repetitions• (Anderson, 1995b; Newell & Simon, 1972; Newell, 1990)

– Problem description = initial state + desired state.– Solution = set of the productions [ initial state desired

state]• choose actions by heuristic functions

( = specified depending on the problem domain )

Related models Connectionistic models

• rulebased reasoning + ( involve distributed | localist representation )

– A two-level neural system (Sun, 1995) • distributed(level 2) and localistic(level 1) representation (Acyclic di-

rected graph)• 1st level : precondition and conclusion localistic, Link to 2nd level's

features• 2nd level : the distributed rules, uncertainty ANN + reinforcement

learning– DCPS: Distributed connectionist production system

(Touretzky, 1985)• production rule = premise + a conclusion

– premise = two triples + matched against the working memory– a conclusion = consists of commands for adding, deleting triples of

the WM• no backtracking and no learning

• Statistical models – recurrent neural nets

• no separation of the problem space and the problem-dependent knowledge

• less transparency

Associative computerIntroduce

• Based on the connectionistic production system– different heuristic functions + learned from experience– The states correspond to pictograms.

• Example domain : the block world • ≡ A production system

– Solves problems = forming a chain of associations• Sequence of actions which lead to a solution of a problem

• Permutation associative memory (Wichert, 2001)• The associations : stored in a new associative memory • learning from experience + using an additional associative memory

Learning from experience– Which associations should be used (heuristics) result

from the distributed representation of the problems

Associative computerStructured binary vector representation

• Structuring– Used by the permutation associative memory– during recognition and execution– without crosstalk and with graceful degradation

• Similarity(Sim)

– a, b : binary pattern vectors, a ≠ b• Quality criterion(qc)

Associative computer Structured representation

• Transition 2 binary pictogram pair• Cognitive entities : Pieces of object for represent scene

– 'what' pathway : visual categorization(Posner, 1994), temporal lobe

– 'where' pathway : parietal lobe

Associative computerrepresentation of Association

• Frame problem (Winston, 1992)– Which part of the description should change and which

not– An empty cognitive entity required

• The accepted uncertainty – Dependent on the threshold value

Associative computerAssociative memory for state transitions

• Associative memory– Model of the long-term memory for sorted Association– A single input several possible associations arise

• cannot be learned by an associative memory (Anderson, 1995)– Nonlinear mechanism is required

• select one or avoid the sum of output branches (Anderson, 1995)new concept : "Tranditional associative memory model"

• not structured pictograms stored in, and represented by binary vec-tors

• Lernmatrix ( Steinbuch )– Permutation associative memory composed Learnma-

trix– Composed of a cluster of units– Unit : simple model of

a real biological neuron– Learning : process of association

• indicate 'one' or 'zero' T : threshold of the unitwij : weight of connection

Associative computerAssociative memory : Detail

• Learning ( binary Hebb rule )– Initialization phase– No information stored– Information = weight ( wij )

• x = question, y = answer

• Retrieval ( x y )– Phase1. recall the appropriate an-

swer• fault tolerant answering macha-

nism– Most similar learned xl

• To the presented question– Hamming distance

appropriate answer

• Backward projection ( y x )– Reverse of Retrieval

• Reliability of the answer– Normalized contrast model

(Smith, 1995; Tversky & Kahne-man,1973)

– xl : x from y by backward projec-tion

Associative computerPermutation associative memory (1)

• δ-permutations of Δ set– A state is represented by Δ cognitive entities

Association = transition between the pictograms– Premise : δ cognitive entities which a correlation of ob-

ject [ should be present ]

– IF State = Premise THEN δ cognitive entities of conclu-sion

• In general : δ << Δ– In the recognition phase

• all possible δ-permutations of Δ cognitive entitiesshould be composed to test if the premise of an association is valid

– In the retrieval phase•

• Ξ permutations are formed– i) question answer– ii) if qc < threshold then associate

– Permutation problem : the reduction of computation of all permutations

Associative computerPermutation associative memory (2)

• Parts– Permute δ arrangement of

entities get same answer before permute• δ parts of the associative memory are permutated

– R ( Parts of ) Associative memory• perform compute parallel

• Constraints : check facts and thresholds– reduce # of possible combinations of

possible associative memories

Associative computerPermutation associative memory (3)

• A model of thalamus– Spotlight theory (Downing & Oinker, 1985)

• visual objects by the brain corresponds• Retrieval : Searchlight model( thalamus )(Crick, 2003)

≒ spotlight– Attention = ∝ a spotlight (Kosslyn, 1994; Posner, 1994)

• cued location and shifted as necessary• by the mechanism of attention window

– Binding stage• associative memory

formed successively

Associative computerProblem Space (1) : Representa-tion

• Representation

– Synchronous : the sequence of the carried out state model

• A state : represented by cognitive entities• A sequence of states of pictograms : described by cognitive entities

can be represented by connected units

Associative computer Problem Space (2) : Linkage

• Linkage – A pattern matcher

• Compute qcCa(b(i ) ) mark chain disable

– Ca = category, b = state• If (qcCa(b(i ) ) = 1 ) then reached

– Backtracker• If [ all units in l is disabled ] then

enabled all units

– Implement Searching algorithm

Associative computer Problem Space (3)

• Pattern heuristics– qcCa(b(i ) ) interpreted by

h#()• h# is heuristic function for cal-

culate distance to desired states

• h0 : Blind-search• h1 : for block world

• Prediction heuristics– Search similar problems to

speed up– Prediction associative

memory• after ‘‘learning’’ the sequence

can be recalled• Learning strategy

– Unsupervised learning– Hebb rules

Associative computerArchitecture

Associative computerExperiments : Geomatrix blocks world