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Artificial general intelligence (AGI) building thinking machines © 2007 General Intelligence Research Group

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AGI vs narrow AI examples of narrow AI: – face recognition – spam filtering – data mining – Google

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Common objections intelligence is not well-defined its too hard computing power is not there yet no unifying theory of AI we dont understand the brain etc… All this is bull shit!

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AI pioneers Alan Turing ( ) John von Neumann ( )

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John McCarthy (1927-) Marvin Minsky (1927-)

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Implications of AGI complete automation ethical issues Technological Singularity Vernor Vinge (1944-) Ray Kurzweil (1948-)

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Representative AGI projects Cyc Soar, ACT-R Polyscheme LIDA SNePS AIXI OSCAR NARS Novamente Cog CAM-Brain HTM SAIL a2i2 and many more…. (listed by Pei Wang)

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Cyc most-funded AI project in history ($10s of millions) based on predicate logic complete ontology millions of facts, concepts Doug Lenat (1950-)

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Soar Allen Newell ( ) John E Laird based on production rules & rete algorithm learning – chunking

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Novamente Ben Goertzel (1966-) probabilistic logic based on uncertain probabilities graph-based knowledge representation genetic algorithms for learning robot living in virtual reality 2007 book: Artificial General Intelligence

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NARS Non-Axiomatic Reasoning System Pei Wang can learn from experience work with insufficient knowledge and resources unified cognition: reasoning, learning, planning, etc… 2006 book: Rigid Flexibility

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SNePS Semantic Network Processing System Stuart C Shapiro extends first-order logic belief revision / assumption-based truth maintenance natural language understanding

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AIXI Marcus Hutter highly abstract based on Kolmogorov complexity theory KC is incomputable learning may take forever!

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Polyscheme Nick Cassimatis integrates multiple methods of representation, reasoning, and problem-solving procedural substrate not one model

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CAM-brain Hugo de Garis (1947-) neural network evolvable hardware cellular automata currently at Wuhan University

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SAIL John Weng neural network-based navigates and learns from environment autonomously

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Jeff Hawkins (1957-) inventor of Palm Pilot founded Redwood Neuroscience Institute 2005 book: On Intelligence HTM (Hierarchical Temporal Memory) neurally-inspired

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Brain- inspired AI visual cortex

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Wiring of 6-layer cortex

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Neurally-inspired AI feedforward neural network Jeff Hawkins approach problem: invariant recognition: translation, rotation, scaling

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Statistical learning takes place in a vector space requires many examples target = manifold difficult to learn concepts with variables eg: On(apple,table), On(car,road), etc…

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Spatial pattern recognition ANN, SVM, PCA, Clustering, etc…

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Logic-based vision visual features logical representation

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Logical-vision example Quadrilateral() :- e 1 :edge e 2 :edge e 3 :edge e 4 :edge v 1 :vertex v 2 :vertex v 3 :vertex v 4 :vertex Connects(e 1,v 1,v 2 ) ^ Connects(e 2,v 2,v 3 ) ^ Connects(e 3,v 3,v 4 ) ^ Connects(e 4,v 4,v 1 )

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Syntactic pattern recognition predicate logic formula: feature i relation 1 (feature 1, feature 2, …) ^ relation 2 (feature 3, feature 4, …) ^ … Spatial interpretation?

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Logic-based AI Avoid reinventing the wheel!

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Logic-based AI first-order predicate logic (Prolog) common objections: brittle rigid binary not numerical just a theorem prover probabilistic / fuzzy logic non-deductive mechanisms eg: abduction, induction

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Modules perception (eg vision) pattern recognition inference natural language learning truth maintenance planning

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Architecture

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Pattern recognition neural characteristics soft computing Prolog: chair(X) :- leg 1, leg 2, leg 3, leg 4, seat, back, horizontal(seat), vertical(back),... leg 1 chair leg 2 leg 3 leg 4 …... fuzzy values

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Pattern recognition – chairs

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more chairs

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still more chairs

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Pattern recognition how humans recognize concepts? [Michalski 1989] 2-tiered approach rule-based vs instance-based Prolog: chair :- chair 1 chair :- chair 2 chair :- chair 3... chair :- (rule for general chair)

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Probabilistic logic classical resolution [JA Robinson 1965] Bayesian networks [eg Judea Pearl]

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Resolution algorithm try to resolve formulas repeatedly until no more can be resolved P V Q~P V R Q V R

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Bayesian network propositional

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First-order Bayes net [Peter Norvig & Stuart Russell 2003] [Kathryn B Laskey 2006] [David Poole 2003] [Manfred Jaeger 1997] etc… BeltStatus(belt)RoomTemp(room) EngineStatus(machine)

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Bayesian vs classical logic Conditional Probability Table (CPT) classical Bayesian (A ^ B) AB C ABC TT1.0 TF0.0 FT FF ABC TTT TFF FTF FFF

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KBMC Knowledge-Based Model Construction [Wellman et al 1992] generate Bayesian networks on-the-fly to answer specific queries KB

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KBMC example

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Belief bases vs belief sets belief set = Cn( belief base ) set of consequences belief sets are too large to manipulate for AGI, must use belief base

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Fuzzy logic Johns girl friend is probably very pretty fuzziness probability Lotfi Zadeh (1921-) 1965 fuzzy sets 1973 fuzzy logic

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Confidence Example: A. 10 girls, 5 have long hair B girls, 500 have long hair p = 0.5 but A and B are not the same B has higher confidence used in Pei Wangs NARS logic

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Probabilistic-fuzzy inference ( P, C, Z ) n ( P, C, Z ) x 1 x 2... Ps and Zs can be point-valued or interval-valued probability confidence fuzziness

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Probability intervals Example: marry fool [p = 0.8] ! marry loser [p = 0.7] p( fool V loser ) = * p( marry ) [ 0.7, 0.8 ] unknown

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Conditional probability table (CPT) All permutations of fuzzy values Or, store in a distribution-free format? abC z1z1 …(P 1, C 1, Z 1 ) z2z2 …(P 2, C 2, Z 2 ) z3z3 …(P 3, C 3, Z 3 ) z4z4 …(P 4, C 4, Z 4 ) ………

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Rules of thought If cats have claws, and Juney is a cat, then Juney has claws. P,x,y P(x) ^ isa(y,x) P(y) modus ponens: syllogisms

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reasoning deduction retroduction inductionabduction

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Abduction finding explanations eg glass is wet it was raining algorithm: reverse of deduction (eg resolution) very high complexity (within the arithmetical complexity class )

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Abduction algorithm

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Induction vs abduction abduction: answer = ground literals eg grass is wet it was raining induction: answer = general formulae eg daughter(X,Y) :- father(Y,X) ^ female(Y)

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Induction learning general patterns statistically ILP (Inductive Logic Programming) [Stephen Muggleton] 1990s

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Induction example Given data: male(mary) = false female(mary) = true mother(mary, louise) = true father(mary, bob) = true daughter(bob, mary) = true daughter(X,Y) :- father(Y,X) ^ female(Y)

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Natural language unifying framework language = knowledge-based inference [Jerry R Hobbs] Abduction as Interpretation eg The Boston office called. apple pie door knob street hawker all we need is a lot of rules can inductively learn the rules

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Belief maintenance Truth Maintenance System (TMS) belief revision to attain consistency avoid cognitive dissonance

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Truth maintenance justifications

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Belief revision Epistemic entrenchmentBelief Base [Mary-Anne Williams 1995] … … entrenchment ranking

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Click feeling Perhaps an effect of successful inference, abduction, or belief revision?

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Paraconsistency holding 2 contradictory beliefs in the knowledge base at the same time

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Associative memory knowledge base = database special indexing to allow associative recall hard disk = long-term memory RAM = working memory

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Planning

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Conclusions neural is problematic blank slate is problematic logic-based is very promising

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Agenda for Logic-based AI 1.design probabilistic-fuzzy logic 2.develop algorithms for: – abduction – belief maintenance 3.acquire common sense knowledge

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Web 2.0-style collaboration branching voting commercial problem: too few members

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Thank you

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[Aliseda 2006] Abductive Reasoning: Logical Investigations into Discovery and Explanation. Synthese Library Series vol 330, Springer [Antoniou 1997] Nonmonotonic Reasoning, MIT Press [Cussens 2001] Integrating probabilistic and logical reasoning. In David Corfield and Jon Williamson eds Foundations of Bayesianism, volume 24 of Applied Logic Series, pages Kluwer, Dordrecht [2000 Flach & Kakas eds] Induction and Abduction, Springer Applied Logic Series #18 [Haddawy 1994] Generating Bayesian networks from probability logic knowledge, in Proceedings of the 10 th conference on uncertainty in AI, [Hobbs 200?] Abduction as Interpretation [Jaeger 1997] Relational Bayesian networks. In Proceedings of the 13 th Annual Conference on Uncertainty in AI (UAI-97), p , San Francisco, CA, 1997, Morgan Kaufman Publishers [Kakas, Kowalski, Toni 1992] Abductive Logic Programming, Journal of Logic and Computation 2(6): [Laskey 2006] MEBN: A logic for open-world probabilistic reasoning. GMU C4I Center Technical Report C4I George Mason Univ, USA. [Milch & Russell 2007] First-Order Probabilistic Languages: Into the Unknown In ILP: Proceedings of the 16th International Conference on Inductive Logic Programming. Berlin: Springer First-Order Probabilistic Languages: Into the Unknown

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[Michalski 1989] Two-tiered concept meaning, inferential matching, and conceptual cohesiveness. In Vosniadou & Ortony eds, Similarity and analogical reasoning, p Cambridge University Press, New York. [Muggleton 1996] Stochastic logic programs. In de Raedt, ed, Advances in Inductive Logic Programming, p , IOS Press [Ngo, Haddawy, & Helwig 1995] A theoretical framework for context- sensitive temporal probability model construction with application to plan projection. In Proceedings of the 11 th Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), p , Montreal, Quebec, Canada. [Norvig & Russell 2003] Artificial Intelligence: A Modern Approach, Prentice Hall. [Poole 1993] Probabilistic horn abduction and Bayesian networks, Artificial Intelligence, 64(1), , 1993 [Poole 2003] First-order probabilistic inference, Proc, IJCAI-03, Acapulco, August 2003, p First-order probabilistic inferenceIJCAI-03 [Wellman, Breese, Goldman 1992] From knowledge bases to decision models. Knowledge Engineering Review 7(1): [Williams 1995] Changing nonmonotonic reasoning inference relations, in Proceedings of the second world conference on the fundamentals of AI, , Ankgor, Paris, 1995

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