.XQVWLJLQWHOOLJHQV01),7K VW )RUHOHVQLQJ. Emne: Subsymbolske metoder

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1 .XQVWLJLQWHOOLJHQV),7K VW )RUHOHVQLQJ Emne: Subsymbolske metoder HYUDOHQHWW - Biologisk basis - Perceptron - Backpropagation (YROXVMRQ UNRPSXWDVMRQ - Genetiske algoritmer - Genetisk programmering - Kunstig liv (Fra første forelesning) INFORMAT IKK STUDIE AV INT ELLIGENTE SYSTEMER RELATERT TIL KOMPUT ASJONELLE PROSESSER er delfelt av REALISEREDATASYSTEMER SOM KAN SIES Å OPP VIS E INT ELLIGENT ADF E R D - DVS. SMARTERE SYSTEMER har er koblet via empirisk vitenskapelig metode vitenskapelig pers pektiv har teknologis k pers pektiv KUNS T IG INT E L L IGE NS (AI) MAT E MAT IKK bygger bl.a. på har har metoder metoder FILOSOFI KOGNIT IV PSYKOLOGI B IOL OGI KUNNSKAPSBASERTE ME T ODE R SUBSYMBOLSKEMET ODER ADFERDSORIENT ERT EMET ODER

2 68%6<%2/6.((72'(5 NEVRALE NETT Distribuert representasjon i et nettverk av noder koblet sammen via lenker (konneksjonisme). Ingen eksplisitt, direkte representasjon av begreper og sammenhenger. GENETISKE ALGORITMER Representasjon i form av bit-strenger som maniplueres vha. genetiske operatorer. Bit-strengene kan representere begreper, men ikke nødvendigvis. Nevrale nett (NN) Alternativt AI-paradigme til kunnskapsbaserte systemer Distribuert - ikke-eksplisitt - representasjon er det mest typiske, men NN trenger ikke være det (lokalistiske nettverk) Kunnskapsrepresentasjons-hypotesen og fysisk-symbol-system hypotesen gjelder ikke for rene NN systemer Relaterte begreper: - Konneksjonsisme - Parallell distribuert prosessering (PDP)

3 Biologis k bakgrunn (løs ): E t nettverk av nerveceller Cell body (95$/((77VWUXNWXURJO ULQJVSULQVLSS Et nettverk av noder delvis koblet i sammen via lenker. Lenkene som går inn til en node kan medføre at noden aktiveres, som igjen medfører at lenken ut fra noden gis en verdi. En node i nettet: o I Z Z Z Z x x4 x2 x3 Aktiveringsverdien for en node (o) bestemmes av en aktiveringsfunksjon (f) der styrken på inn-lenkene (x..) samt av vektingen av hver av dem (w..) er input-parametre. Nevrale nett trenes opp ved at inndata gis inn, utdata registreres og avvikende resultatet tilbakeføres til nettet slik at vektene på lenkene blir justert i riktig retning

4 3HUFHSWURQ - enkelt-lags nett - binære input- og aktiverings-verider (- +) - vekter er reelle tall - aktiveringsfunksjonen er er en trappe-terskelfunksjon - lærings-regel for å oppdatere vektene: w i = w i + c(d - sign(sum i x i w i ))x i - konvergerer KYLVV input-verdiene danner OLQH UWVHSDUHUEDUHklasser )OHUODJVQHWW - Introduksjon av et skjult lag gjør at begrensningen om lineær separerbarhet kan omgås - konvergens kan ikke garanteres generelt men for praktiske tilfeller vises - input- og ut-verdier ikke kun binære - aktiveringsfunksjonen mer generell enn for Perceptroner - Backpropagation læringsmetode - en generalisering av regelen for Perceptroner -> Delta-regelen for læring

5 Egnede DQYHQGHOVHU av NN - Problemer der det er vanskelig å identifisere og beskrive kunnskap på eksplisitt form. - F.eks.: tolking av bilder, tolking og syntese av tale. Klassisk eksempel: NETTALK - System for kunstig tale (tekst -> tale) - Basert på Backpropagation - Oppsett lignende som CBRTALK (Forel. 6, se neste to transps) - Bestemmer fonem for bokstav midt i 7-bokstavs vindu, der fonemet avhenger av de 3 bokstaver på hver side - Nettkonfigurasjon: - 29 input enheter (26 bokstaver +., ) - 26 ut-enheter (2 lyder + 5 trykk og betoninger) - 3-lags nett med 8 skjulte noder (Fra CBR tema - Memory-Based Reasoning) MBR-talk (Stanfill&Waltz 86) Learns to pronounce english words A word is represented in a 9-letter window ****file* f + ***file** A **file*** l - *file**** - - Compared to NET-talk

6 (Fra CBR tema) MBR - Experiment words in database (here: = case base) - new words in test set MBR-talk Dictionary evaluation: Correct phonemes: 86 % of cases Correct word 43 % of cases Human judgement of word pronounciation: Good: 47% Net-talk After 3. trials: Correct phonemes: 78 % of cases Adferdsbaserte metoder Fra kognitive til biologiske - og etologiske - modeller av intelligent adferd. Intelligent adferd forårsakes ikke av modeller som er representert i maskinen, den oppstår i vekselvirkning med omgivelsene. Basis for det nye fagfeltet 'Artificial Life'.

7 Darwinian Evolution Physiological, Behavioral Phenotypes Natural Selection Ptypes Morphogenesis Reproduction Sex Genotypes Recombination & Mutation Gtypes Genetic Evolutionary Algorithms Parameters, Code, Neural Nets, Rules Semantic Performance Test P,C,N,R Translate R &M Generate Bit Strings Syntactic Recombination & Mutation Bits

8 Evolutionary Computation = Parallel Stochastic Search,QGLY )LWQHVV Translation & Performance Test Selection Biasing Biased Roulette Wheel Selection Next Generation Mutation Crossover Types of Evolutionary Algorithms Genetic Algorithms (Holland, 975) Representation: Bit Strings => Integer or real feature vectors Syntactic crossover (main) & mutation (secondary) Evolutionary Strategies (Recehenberg, 972; Schwefel, 995) Representation: Real-valued feature vectors Semantic mutation (main) & crossover (secondary) Evolutionary Programs (Fogel, Owens & Walsh, 966; Fogel, 995) Representation: Real-valued feature vectors or Finite State Machines Semantic mutation (only) View each individual as a whole species, hence no crossover Genetic Programs (Koza, 992) Representation: Computer programs (typically in LISP) Syntactic crossover (main) & mutation (secondary)

9 Evolutionary Computation Requirements Domain that supports quantitative fitness assignment Fitness function that accurately evaluates performance Representation for solutions that tolerates mutation & crossover Classic Genetic Algorithm P P2 P3 7 4 X Travelling Salesman Problem (TSP) Given: N cities & matrix of distances between them. Find: Shortest cyclic tour that visits all cities. NP-Hard: Exponential to both find solutions & to verify solutions Heuristic Methods: Find optimal solutions when N<. Genetic Algorithm: Find good solutions for any N Applications: Network building, Delivery routing, Sequence scheduling...

10 Applying GAs to TSP Fitness Function: /tour-length or optimal-tour-length/tour-length Chromosome: Direct Representation: List of cities (standard approach) Indirect Representation: List of next city to pull from ordered list and insert into the solution sequence => Crossover Standard Bit or Integer Cross: Only works for indirect representations Location Preserving: Children inherit, as much as possible, cities in same gene location as parents Edge Preserving: Children inherit, as much as possible, city-city edges from parents (actual edge locations in the chromosome may vary from parent to kid) *Crossover is the key element to TSP GA s - and where most research is done. Representational Issues for GA-based TSP x 6; x 4 X x 4; x 6 Standard Crossover & Mutation are purely syntactic => pay no attention to semantics (i.e. The meanings of the bits or integers that they manipulate). Direct representations often embody constraints that simple crossover & mutation cannot enforce. Indirect representations usually involve fewer constraints, so simple crossover & mutation are often sufficient. Compare to Process Scheduling (Kidwell), where constraints (i.e. All alleles between and N) were easy to enforce.

11 Inheritance For GA s to make progress, they must pass on many of the good features from generation G to generation G+. Hence, when parents crossover, the good features of each should be preserved in at least one of the children. Biology: Heritability = degree to which children resemble their parents (X kid-avg - X pop-avg ) = heritability*(x parent-avg -X pop-avg ) Location Preservation ( ) With PMX & Subtour exchanges, kids inherit many cities in the same position as in one of the parents. Results not that promising => GA viewed as inappropriate for TSP. Edge Preservation (989 - present) Edges are the key contributors to TSP costs (fitness), so what we really need to preserve are city pairs (i.e. Edges) of TSP tours. GA s with edge focus perform much better, near optimal => GA useful for TSP! Using Evolutionary Algorithms When Large, rough search spaces Satisficing or Optimization problems Entire solutions are easily generated and tested Exhaustive search methods are too slow Heuristic search methods cannot find good solutions (e.g. Stuck at local max) How Determine EA-amenable representation of solutions Define fitness function Define selection function = roulette-wheel biasing function (f: fitness -> area) Set key EA parameters: population size, mutation rate, crossover rate, # generations, etc. * EA s are easy to write, and there s lots of freeware! * Specific problems often require specific representations & genetic operators

12 Application Areas for Evolutionary Algorithms Optimization: Controllers, Job Schedules, Networks(TSP) Electronics: Circuit Design (GP) Finance: Stock time-series analysis & prediction Economics: Emergence of Markets, Pricing & Purchasing Strategies Sociology: cooperation, communication, ANTS! Computer Science Machine Learning: Classification, Prediction Algorithm design: Sorting networks Biology Immunology: natural & virtual (computer immune system) Ecology: arms races, coevolution Population genetics: roles of mutation, crossover & inversion Evolution & Learning: Baldwin Effect, Lamarckism Artificial Life Biology = study of carbon-based life life as we know it Alife = study of the dynamics of living systems, regardless of substrate. life as it could be Substrates: abstract chemistries, logical networks, cellular automata, abstract ecosystems, emulated computers..

13 Emergence & Self-Regulation The signal feature of life is not the carbon-based substrate...(but)...that the local dynamics of a set of interacting entities (e.g. molecules, cells, etc.) supports an emergent set of global dynamical structures which stabilize themselves by setting the boundary conditions within which the local dynamics operates (Charles Taylor, biologist, UCLA) Global Structure Constraints Emergence Agents Properties of Alife Systems Synthetic: Bottom-up, multiple interacting agents Self-regulating: No global/centralized control. Self-Organizing: Global structure is emergent. Adaptive: Learning and/or evolving Complex: On the edge of chaos; dissipative

14 Relevant Domains for Alife Individual organisms: physiology, behavior, ontogeny Social insect colonies (Ants!!): emergence of superorganism Populations of organisms: evolution Ecosystems: emergence & evolution MicroEconomics: populations of buyers & sellers Sociology: emergence & evolution of societies Traffic: emergence of flow/jam patterns Robotics: Emergence of Intelligent behaviors w/o global control Abstract computational models: logic, chemistry, physics Why Study Alife? Understanding Emergent Phenomena Synthetic approaches -vs- analytic reductionism Chaos, complexity, self-organization Biological Research Test effects of local behaviors upon populations Genetic Engineering Computer Science Nanotechnology, Animation AI: Intelligence Architectures, Evolutionary Computation Educational Toolkits: Social systems (SimCity) Ecosystems (SimLife, SimEarth), Economical systems

15 What s a living system? (Capra, The Web of Life (996) Pattern: Autopoietic Network Self-bounded Self-generating Self-perpetuating Structure: Dissipative Far From Equilibrium Edge of Chaos Self-Organized Criticality Process: Cognition Embodiment of pattern within structure Life & Cognition are inseparable Cellular Automata (ex.) Step N Step N+ Update rule: If exactly 2 dark neighbors, change to dark; else change to light.