A. Aamodt, NTNU-IDI Snapshots of AI methods and applications Agnar Aamodt and Keith Downing Institutt for datateknikk og informasjonsvitenskap Seksjon.

Presentasjon om: "A. Aamodt, NTNU-IDI Snapshots of AI methods and applications Agnar Aamodt and Keith Downing Institutt for datateknikk og informasjonsvitenskap Seksjon."— Utskrift av presentasjonen:

1 A. Aamodt, NTNU-IDI Snapshots of AI methods and applications Agnar Aamodt and Keith Downing Institutt for datateknikk og informasjonsvitenskap Seksjon for Intelligente Systemer NTNU

2 A. Aamodt, NTNU-IDI Hva er “Kunstig intelligens” – 1 “AI = Things that make you go WOW!” eller…?? vel, mer edruelig - om enn litt kjedeligere - så er kjerneideen: “AI = Representation + Search” The concept of search plays an important role in science and engineering In one way, any problem whatsoever can be seen as a search for “the right answer”

3 A. Aamodt, NTNU-IDI  Software: Pro-aktive beslutningsstøtte- systemer Automatisk data-analyse Lærende systemer, f.eks.: –Anbefalingssystemer –AI i spill –Ansiktsgjenkjenning Naturlig språk Robotnavigering, syn, planlegging Adapterende GUI...  Embedded systems –Intelligente komponenter i totalsystemer (hardware + software)  Annen hardware: –Autonome roboter Online bildefortolking Samarbeid Planleggingssystemer …  Hjernesimulering Kognisjonsvitenskap Selvorganiserende systemer … Example applications

4 A. Aamodt, NTNU-IDI har teknologisk perspektiv har metoder STUDIET AV INTELLIGENTE SYSTEMER RELATERT TIL KOMPUTASJONELLE PROSESSER REALISERING AV DATASYSTEMER SOM KAN SIES Å OPPVISE INTELLIGENT ADFERD - DVS. ' SMARTERE ' SYSTEMER SYMBOLORIENTERTE (KUNNSKAPSBASERTE METODER) METODER SUBSYMBOLSKE (BIO-INSPIRERTE METODER) METODER KOGNITIV PSYKOLOGI FILOSOFI bygger bl.a. på MATEMATIKK BIOLOGI KUNSTIG INTELLIGENS (AI) har vitenskapelig perspektiv er koblet via empirisk vitenskapelig metode INFORMATIKK er delfelt av Hva er “Kunstig intelligens” – 2 har metoder

5 A. Aamodt, NTNU-IDI KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Heuristiske regler Regelbaserte systemer (f.eks.: MYCIN)

6 A. Aamodt, NTNU-IDI Kontroll-kunnskap Heuristiske regler KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Eksplisitt kontrollkunnskap(f.eks. NEOMYCIN) - kunnskap om typer regler for typer tilstander

7 A. Aamodt, NTNU-IDI Kontroll-kunnskap Heuristiske regler KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Dypere modeller, lærebok-kunnskap (f.eks. CASNET) - flere relasjoner, semantiske nett, rammer Dyp kunnskap

8 A. Aamodt, NTNU-IDI Kontroll-kunnskap Heuristiske regler Spesifikke case Dyp kunnskap KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Fra generell kunnskap til situasjons-spesifikke case (f.eks. CYRUS, PROTOS) - case-basert resonnering

9 A. Aamodt, NTNU-IDI The Case-Based Reasoning (CBR) Cycle (Aamodt&Plaza 1994)

10 A. Aamodt, NTNU-IDI Kontroll-kunnskap Heuristiske regler Spesifikke case Dyp kunnskap KUNNSKAPSBASERTE METODER - UTVIKLINGSTRENDER Integrerte systemer(f.eks. SOAR, CREEK, META-AQUA) - totalarkitekturer for intelligent problemløsning

11 A. Aamodt, NTNU-IDI VIDEO CLIP Herb Simon

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13 VIDEO CLIP

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15 Subsymbolic / Bio-inspired AI Methods

16 A. Aamodt, NTNU-IDI Emergence 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)

17 A. Aamodt, NTNU-IDI Swarm Intelligence Follow Trail Find Food Make Trail

18 A. Aamodt, NTNU-IDI Termite Arch-Building (Stigmergy) pheremone Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds (Resnick, 1994)

19 A. Aamodt, NTNU-IDI Columns to Arches Positive Feedback: Pheromone Concentration in middle gets higher and higher as more dirt balls are added.

20 A. Aamodt, NTNU-IDI Boids (Craig Reynolds) http://www.red3d.com/cwr/boids/

21 A. Aamodt, NTNU-IDI Ubiquity of Emergence

22 A. Aamodt, NTNU-IDI Emergence & Intelligence Emergence Spectrum How does intelligent behavior arise from the interactions of 100 billion neurons, without central control? How has the brain evolved?

23 A. Aamodt, NTNU-IDI Evolutionary Progressions along the Intelligence Spectrum Living organisms Computers Sense & Act: 10,000,000+ years. 15+ years Reason: 100,000+ years. 30+ years Calculate: 1,000+ years 50+ years Evolution of reasoning was tightly constrained and influenced by sensorimotor capabilities. Else extinction! GOFAI systems are often in their own little worlds, making unreasonable assumptions about independent sensorimotor apparatus. To achieve AI’s scientific goal of understanding human intelligence, the road from sense-and-act to reasoning via simulated evolution may be the only way.

24 A. Aamodt, NTNU-IDI Cognitive Incrementalism Tacit assumption of SEAI research. Cognition (and hence common sense) is an extension of sensorimotor behavior. This is the idea that you do indeed get full-blown, human cognition by gradually adding ’bells and whistles’ to basic (embodied, embedded) strategies of relating to the present at hand…Mindware, pg. 135 (Andy Clark, 2001). I am, therefore I think. Brooks, Steels, Pfeifer, Scheier, Beer, Thelens, Nolfi, Floreano…

25 A. Aamodt, NTNU-IDI Darwinian Evolution Genotypes Phenotypes Morphogenesis Natural Selection Recombination & Mutation Ptypes Gtypes Reproduction Sex Genetic Physiological, Behavioral

26 A. Aamodt, NTNU-IDI Evolutionary Algorithms Bit Strings Parameters, Code, Neural Nets, Rules Translate Performance Test Recombination & Mutation P,C,N,R Bits Generate Semantic Syntactic R &M

27 A. Aamodt, NTNU-IDI Artificial Neural Networks

28 A. Aamodt, NTNU-IDI World Model Behav Gen Body World Brain GOFAI World Model Behav Gen Body World Brain SEAI The world is its own best model… Rodney Brooks World Model Behav Gen Body World Connectionism

29 A. Aamodt, NTNU-IDI GOFAI -vs- SEAI Brittle Nerds -vs- Well-Rounded Insects Knowledge Selection Pressure Knowledge Cramming -vs- Adaptive Systems GOFAI SEAI

30 A. Aamodt, NTNU-IDI A master thesis in AI at IDI – a few examples

31 A. Aamodt, NTNU-IDI IDIs Seksjon for Intelligente Systemer - Organisering i 3 faggrupper Kunnskapsbaserte systemer –Case-basert resonnering –Kunnskapsmodellering –Intelligente agenter –Adaptive brukergrensesnitt –Usikkerhetsbehandling/grafiske modeller –Bildebehandling/kunstig syn –Maskinlæring/datamining. Selvorganiserende systemer –Evolusjonære metoder –Konneksjonisme –Nevrovitenskap –Kunstig liv –Maskinlæring Språkteknologi –Naturlig språklig fortåelse –Beregnbar logikk –Tekstmining –BusTuc 31 ansatte: –11 heltidsstillinger –4 Deltid –3 Forskere –13 PhD studenter 20 – 25 MSc studenter per år

32 A. Aamodt, NTNU-IDI Improved game AI through case-based and statistical reasoning Eksempler på master-oppgaver

33 A. Aamodt, NTNU-IDI Eksempler på master-oppgaver

34 A. Aamodt, NTNU-IDI Eksempler på master-oppgaver

35 A. Aamodt, NTNU-IDI Bilde- og/eller Video-analyse (Her: Segmentere bilder av karbonfiberarmert epoxy) Eksempler på master-oppgaver

36 A. Aamodt, NTNU-IDI Bilde- og/eller Video-analyse (Her: Segmentere bilder av fisk i Mauritius) Eksempler på master-oppgaver

37 A. Aamodt, NTNU-IDI Robots (pictured) that interact with either a real or simulated other robot. Within our PUCKER system, researchers and students can easily test their AI control strategies on this type of robot (e- pucks). Eksempler på master-oppgaver

38 A. Aamodt, NTNU-IDI Intelligent Hardware Today’s hardware technologies, especially Field programmable Gate Arrays (FPGAs), provide many possibilities for the creation of intelligent Hardware - that is AI techniques embedded in hardware. Such embedding may be for the purpose of speed-up of a given AI technique for perhaps real-time application requirements or for the purpose of creating hardware circuits, applying bio- inspired techniques as the design technique. The latter is known as the field of Evolvable Hardware and includes applications in today’s technology and approaches to achieve computation in tomorrow’s technology. Application areas range from Vision, art to electronic circuits. Eksempler på master-oppgaver

39 A. Aamodt, NTNU-IDI Eksempler på master-oppgaver Språkteknologi - maskinoversetting

40 A. Aamodt, NTNU-IDI Eksempler på master-oppgaver

41 A. Aamodt, NTNU-IDI Discovery of causal relations in incident reports An incident report (i.e., a 'textual case') describes how a problem unfolds. That is, the story starts with less important 'symptoms'/evidence which, in turn, triggers/causes more serious ones, and this chain of evidence ends up with an undesired, anomalous event. It is important to identify the events when they are small, and discover the causal mechanisms underlying the chain of events. Use of eye-tracking in the selection of important features in a text and determining how important they are - the latter is called 'weighting’. This in cooperation with people at Dragvoll. Eksempler på master-oppgaver Textual CBR.

42 A. Aamodt, NTNU-IDI Computer Assisted Assessment and Treatment of Pain Probabilistic networks, Rules, CBR, meta-level reasoning Eksempler på master-oppgaver

43 A. Aamodt, NTNU-IDI Data mining and Decision support in Fish Farming Eksempler på master-oppgaver

44 A. Aamodt, NTNU-IDI Evolving Populations of Social Insects to Perform Annular Sorting P = Pick upD = Deposit F = ForwardB = Backward L = LeftR = Right Sensing Acting Vegard Hartmann Andre Hei Vik Eksempler på master-oppgaver

45 A. Aamodt, NTNU-IDI Fitness Evaluation Eksempler på master-oppgaver

46 A. Aamodt, NTNU-IDI Three-object annular structure Eksempler på master-oppgaver

47 A. Aamodt, NTNU-IDI One day of unwanted downtime on this rig means increased cost of 1,6 MNOK for the ongoing drilling operation. Providing the relevant experience and getting the right information precisely when needed will reduce unwanted operational downtime. The result is a more reliable drilling process, reduced drilling costs, and increased productivity. Reducing unwanted downtime in oil drilling Eksempler på master-oppgaver

48 A. Aamodt, NTNU-IDI Improved decision support through experience capture and reuse - pattern analysis - case-based reasoning Eksempler på master-oppgaver

49 A. Aamodt, NTNU-IDI VIDEO CLIP Eksempler på master-oppgaver

50 A. Aamodt, NTNU-IDI DIS har deltatt i etablering av tre spin-off selskaper: - LingIT AS - naturlig språk tolkning og dialogsystemer - Trollhetta AS - bildeanalyse og beslutningsstøtte - Verdande Technology AS - erfarings-lagring og aktiv gjenbruk, primært innen oljeboring

51 A. Aamodt, NTNU-IDI AI - covers a lot of methods and application areas - is interesting, useful, and fun So, learn your - basic AI formalisms, such as - logics - representations - state-space search methods Link to videos shown (and more!): http://videolectures.net/aaai07/ http://videolectures.net/aaai08/ http://videolectures.net/ijcai09_video_competition/ A useful link to all of AI: http://www.aaai.org/aitopicshttp://www.aaai.org/aitopics

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53 Evolutionary Computation