Overview of Artificial Intelligence

1. Overview of Artificial Intelligence Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University

2. 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

3. 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...

4. 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 strong methods: Inference Planning

5. Search Algorithms • Define state representation • Define operators (fn: stateneighbor states) • Define goal (criteria) • Given initial state (S0), generate state space S0

6. 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

7. 1 Depth-first search (DFS) 2 12 3 6 8 13 14 4 5 7 9 10 11 15 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) 1 Breadth-first search (BFS) 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

8. 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

9. 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

10. CSP algorithms operate on the constraint graph • VariablesWA, NT, Q, NSW, V, SA, T • DomainsDi = {red,green,blue} • Constraints: adjacent regions must have different colors, e.g., WA ≠ NT

11. Planning • How to transform world state to achieve goal? • operators represent actions • encode pre-conditions and effects in logic pre-conds: x ingredient(x,cake) dry(x)have(x) pre-conds: mixed(dry_ingr)& mixed(wet_ingr) goto kitchen mix dry ingredients effect: mixed(dry_ingr) transfer ingredients from bowl to pan Initial state: in(kitchen) have(eggs) have(flour) have(sugar) have(pan) ~have(cake) sautee Goal: have(cake) bake at 350 buy milk start car apply frosting mix wet ingredients pre-cond: baked goto store another example to think about: planning rescue mission at disaster site

12. Planning • How to transform world state to achieve goal? • operators represent actions • encode pre-conditions and effects in logic pre-conds: x ingredient(x,cake) dry(x)have(x) pre-conds: mixed(dry_ingr)& mixed(wet_ingr) goto kitchen mix dry ingredients effect: mixed(dry_ingr) transfer ingredients from bowl to pan Initial state: in(kitchen) have(eggs) have(flour) have(sugar) have(pan) ~have(cake) sautee Goal: have(cake) bake at 350 buy milk start car apply frosting mix wet ingredients pre-cond: baked goto store another example to think about: planning rescue mission at disaster site

13. Planning Algorithms • 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 have(cake) <= baked(cake)&have(frosting) <=...

14. 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

15. 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

16. D Inference Algorithms A&BD • 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 B A BvC ~C

17. 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

18. 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)

19. 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 P X f Y a f b Y b

20. 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?

21. 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

22. 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

23. 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

24. initial state perception goals KB action goal state environment agent