인지기반 지능형 에이전트 설계 : 인식 Associative computer: a hybrid connectionistic production system

1. 인지기반 지능형 에이전트 설계: 인식Associative computer: a hybrid connectionistic production system Action Editor : John Barnden 발제 : 최 봉환, 04/07, 2009

2. 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

3. 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)

4. 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 objects • 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 & Sloman, 1994). • items = ( vectors or vector parts ) <> symbols (Anderson, 1995a; Ga¨rdenfors, 2000; McClelland & Rumelhart, 1985; Wichert, 2000, 2001).

5. 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 )

6. 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 directed 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

7. 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

8. 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)

9. 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

10. 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

11. 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 vectors • Lernmatrix ( Steinbuch ) • Permutation associative memory  composed Learnmatrix • 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 unit • wij : weight of connection

12. 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 answer • fault tolerant answering machanism • 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 & Kahneman,1973) • xl : x from y by backward projection

13. Associative computerPermutation associative memory (1) • δ-permutations of Δ set • A state is represented by Δ cognitive entities  Association = transition between the pictograms • Premise : δcognitive entities which a correlation of object [ should be present ] • IF State = Premise THEN δcognitive entities of conclusion • In general : δ<< Δ • In the recognition phase • all possible δ-permutations of Δcognitive 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

14. 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 ofpossible associative memories

15. 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 memoryformed successively

16. Associative computerProblem Space (1) : Representation • 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

17. 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 ] thenenabled all units •  Implement Searching algorithm

18. Associative computer Problem Space (3) • Pattern heuristics • qcCa(b(i))  interpreted by h#() • h# is heuristic function for calculate 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

19. Associative computerArchitecture

20. Associative computerExperiments : Geomatrix blocks world