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Lecture4 (1)
 

Lecture4 (1)

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    Lecture4 (1) Lecture4 (1) Presentation Transcript

    • Overview of Artificial Intelligence Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University
    • What is AI? • Real applications, not science fiction – Control systems, diagnosis systems, games, interactive animations, combat simulations, manufacturing scheduling, transportation logistics, financial analysis, computer-aided tutoring, search- and-rescue robots
    • Different Perspectives • Philosophical perspective – What is the nature of “intelligence”? Can a machine/program ever be truly “intelligent”? – Strong AI hypothesis: Is acting intelligently sufficient? – laws of thought; rational (ideal) decision-making • Socrates is a man; men are mortal; therefore, Socrates is mortal • Psychological perspective – What is the nature of “human intelligence”? – Cognitive science – concept representations, internal world model, information processing metaphor – role of ST/LT memory? visualization? emotions? analogy? creativity? – build programs to simulate inference, learning...
    • • Mathematical perspective – Is “intelligence” a computable function? – input: world state, output: actions – Can intelligence be systematized? (Leibnitz) – just a matter of having enough rules? – higher-order logics for belief, self-reference • Engineering (pragmatic) perspective – AI helps build complex systems that solve difficult real- world problems – decision-making (agents) – use knowledge-based systems to encode “expertise” (chess, medicine, aircraft engines...) sense decide act weak methods: Search Planning strong methods: Inference
    • Search Algorithms • Define state representation • Define operators (fn: state→neighbor states) • Define goal (criteria) • Given initial state (S0), generate state space S0
    • Many problems can be modeled as search • tic-tac-toe – states=boards, operator=moves • symbolic integration – states=equations, opers=algebraic manipulations • class schedule – states=partial schedule, opers=add/remove class • rock band tour (traveling salesman problem) – states=order of cities to visit, opers=swap order • robot-motion planning – states=robot configuration, opers=joint bending
    • 1 2 12 3 6 8 13 14 4 5 7 9 10 11 15 1 2 43 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Depth-first search (DFS) Breadth-first search (BFS) Notes: recursive algorithms using stacks or queues BFS often out-performs, due to memory limits for large spaces choice depends on complexity analysis: consider exponential tree size O(bd )
    • Heuristics • give guidance to search in terms of which nodes look “closest to the goal” – node evaluation function – h(n)=w1*(piece_differential)+w2*(center_control)+ w3*(#pieces_can_be_taken)+w4*(#kings) • greedy algorithms search these nodes first • bias direction of search to explore “best” parts of state space (most likely to contain goal) • A* algorithm – optimal (under certain conditions) – finds shortest path to a goal – insensitive to errors in heuristic function
    • Specialized Search Algorithms • Game-playing – two-player zero-sum games (alternate moves) – minimax algorithm: form of “look-ahead” – If I make a move, how will opponent likely respond? Which move leads to highest assured payoff? • Constraint-satisfaction problems (CSPs) – state=partial variable assignment – goal find assignment that satisfies constraints – algorithms use back-tracking, constraint propagation, and heuristics – pre-process constraint-graph to make more efficient – examples: map-coloring, propositional satisfiability, server configuration
    • • Variables WA, NT, Q, NSW, V, SA, T • Domains Di = {red,green,blue} • Constraints: adjacent regions must have different colors, e.g., WA ≠ NT CSP algorithms operate on the constraint graph
    • Planning • How to transform world state to achieve goal? • operators represent actions – encode pre-conditions and effects in logic Initial state: in(kitchen) have(eggs) have(flour) have(sugar) have(pan) ~have(cake) Goal: have(cake) mix dry ingredients mix wet ingredients transfer ingredients from bowl to pan bake at 350 apply frosting pre-conds: ∀x ingredient(x,cake) &dry(x)→have(x) effect: mixed(dry_ingr) pre-conds: mixed(dry_ingr)& mixed(wet_ingr) pre-cond: baked goto kitchen goto store start car buy milk sautee another example to think about: planning rescue mission at disaster site
    • Planning • How to transform world state to achieve goal? • operators represent actions – encode pre-conditions and effects in logic Initial state: in(kitchen) have(eggs) have(flour) have(sugar) have(pan) ~have(cake) Goal: have(cake) mix dry ingredients mix wet ingredients transfer ingredients from bowl to pan bake at 350 apply frosting pre-conds: ∀x ingredient(x,cake) &dry(x)→have(x) effect: mixed(dry_ingr) pre-conds: mixed(dry_ingr)& mixed(wet_ingr) pre-cond: baked goto kitchen goto store start car buy milk sautee another example to think about: planning rescue mission at disaster site
    • Planning Algorithms have(cake) <= baked(cake)&have(frosting) <=... • State-space search – search for sequence of actions – very inefficient • Goal regression – work backwards from goal – identify actions relevant to goal; make sub-goals • Partial-order planning – treat plan as a graph among actions – add links representing dependencies • GraphPlan algorithm – keep track of sets of achievable states; more efficient • SatPlan algorithm – model as a satisfiability problem
    • Knowledge-Based Methods • need: representation for search heuristics and planning operators • need expertise to produce expert problem-solving behavior • first-order logic – a formal language for representing knowledge • rules, constraints, facts, associations, strategies... – rain(today)→wet(road) – fever→infection – in(class_C_air_space)→reduce(air_speed,150kts) – can(take_opp_queen,X)&~losing_move(X)→do(X) • use knowledge base (KB) to infer what to do – goals & initial_state & KB do(action) – need inference algorithms to derive what is entailed • declarative vs. procedural programming
    • First-Order Logic • lingua franca of AI • syntax – predicates (relations): author(Candide,Voltaire) – connectives: & (and), v (or), ~ (not), → (implies) – quantified variables: ∀X person(X)→∃Y mother(X,Y) • Ontologies – systems of concepts for writing KBs – categories of stuff (solids, fluids, living, mammals, food, equipment...) and their properties – places (in), part_of, measures (volume) – domain-dependent: authorship, ambush, infection... – time, action, processes (Situation Calculus, Event Logic) – beliefs, commitments • issues: granularity, consistency, expressiveness
    • Inference Algorithms • Natural deduction – search for proof of query – use rules like modus ponens (from A and A→B, get B) • Backward-chaining – start with goal, reduce to sub-goals – complete only for definite-clause KBs (rules with conjunctive antecedents) • Resolution Theorem-proving – convert all rules to clauses (disjunctions) – {AvB,~BvC}→AvC – keeping resolving clauses till produce empty clause – complete for all FOL KBs D A&B→D A BvC ~C B
    • Prolog and Expert Systems • Automated deduction systems • programming = writing rules • make query, system responds with true/false plus variable bindings • inference algorithm based on backward-chaining
    • Prolog example sibling(X,Y) :- parent(Z,X), parent(Z,Y). grandfather(X,Y) :- father(X,Z),parent(Z,Y). parent(X,Y) :- father(X,Y). parent(X,Y) :- mother(X,Y). mother(tracy, sally). father(bill, sally). father(bill, erica). father(mike, bill). ?- sibling(sally,erica). Yes ?- grandfather(sally,X). grandfather(sally,mike)
    • • Unification Algorithm – determine variable bindings to match antecedents of rules with facts – unif. algorithm traverses syntax tree of expressions – P(X,f(Y),Y) matches P(a,f(b),b) if {X/a,Y/b} – also matches P(a,f(a),a) – does not match P(a,b,c), P(b,b,b) P X f Y Y P a f b b
    • • Managing Uncertainty in real expert systems – default/non-monotonic logics (assumptions) – certainty factors (degrees of beliefs) – probabilistic logics – Bayesian networks (causal influences) • Complexity of inference? – suitable for real-time applications?
    • Application of Data Structures and Algorithms in AI • priority queues in search algorithms • recursion in search algorithms • shortest-path algorithm for planning/robotics • hash tables for indexing rules by predicate in KBS • dynamic programming to improve efficiency of theorem-provers (caching intermediate inferences) • graph algorithms for constraint-satisfaction problems (arc-consistency) • complexity analysis to select search algorithm based on branching factor and depth of solution for a given problem
    • Use of AI in Research • intelligent agents for flight simulation – collaboration with Dr. John Valasek (Aerospace Eng.) – goal: on-board decision-making without ATC – approach: use 1) multi-agent negotiation, 2) reinforcement learning • pattern recognition in protein crystallography – collaboration with Dr. James Sacchettini (Biochem.) – goal: automate determination of protein structures from electron density maps – approach: extract features representing local 3D patterns of electron density and use to recognize amino acids and build – uses neural nets, and heuristics encoding knowledge of typical protein conformations and contacts
    • • TAMU courses on AI – CPSC 420/625 – Artificial Intelligence – undergrad • CPSC 452 – Robotics and Spatial Intelligence • also related: CPSC 436 (HCI) and CPSC 470 (IR) – graduate • CPSC 609 - AI Approaches to Software Engineering* • CPSC 631 – Agents/Programming Environments for AI • CPSC 632 - Expert Systems* • CPSC 633 - Machine Learning • CPSC 634 Intelligent User Interfaces • CPSC 636 - Neural Networks • CPSC 639 - Fuzzy Logic and Intelligent Systems • CPSC 643 Seminar in Intelligent Systems and Robotics • CPSC 644 - Cortical Networks • CPSC 666 – Statistical Pattern Recognition (not official yet) • Special Topics courses (CPSC 689)... • * = not actively taught
    • goals KB initial state goal state perception action agent environment