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Artificial Intelligence 15-381

Artificial Intelligence 15-381. Introduction to AI & Search Methods I Jaime Carbonell jgc@cs.cmu.edu 28 August 2001. Today’s Topics . Are we in the right class? What exactly is AI, anyway? AI = search+knowledge+learning AI application areas Course Outline Administration and grading 

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Artificial Intelligence 15-381

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  1. Artificial Intelligence 15-381 Introduction to AI & Search Methods I Jaime Carbonell jgc@cs.cmu.edu 28 August 2001

  2. Today’s Topics • Are we in the right class? • What exactly is AI, anyway? • AI = search+knowledge+learning • AI application areas • Course Outline • Administration and grading  • Basic search methods

  3. What is AI: Some Quick Answers From the Media: AI is… • …What socially-inept superhackers do • …The opposite of natural stupidity • …Building useful idiot-savant programs • …Deep Blue (IBM’s chess program) • …Robots with feelings (Spielberg)

  4. What is AI: Some Quick Answers (cont.) From Academia: AI is… • …modeling aspects of human cognition by computer • …the study of solving ill-formed problems • …"nothing more" than advanced algorithms research • …cool stuff! Machine learning, data mining, speech, language, vision, web agents…and you can actually get paid a lot for having fun! • …what other CS folks don’t yet know how to do, and we AIers aren’t always too sure either

  5. Operationally Speaking, AI is: Applied Cognitive Science • Computational models of human reasoning • Problem solving • Scientific thinking • Models of non-introspective mental processes • Language comprehension, language learning • Human memory organization (STM, LTM)

  6. Operationally Speaking, AI is: Knowledge Engineering • Codify human knowledge for specific tasks • E.g.: Medical diagnosis, Machine Translation • Central in 1970s & 80sjust one lecture here Problem-Solving Methods • How to encode and use knowledge to find answer • E.g. HS, MEA, A*, Logic resolution • Always at the very core of AImany lectures

  7. Operationally Speaking, AI is: Machine Learning • Learning as the hallmark of intelligence…but it is already practical in multiple applications • E.g.: D-trees, rule-induction, reinforcement, NNets • Discredited in 1960s Vibrant core in 1990s • Applications: data & text mining, speech, robotics • Most active research area in AI many lectures

  8. AI “Application” Areas Rule-Based Expert Systems • Medical Diagnosis: MYCIN, INTERNIST, PUFF • CSP Scheduling: ISIS, Airline scheduling Data Mining • Financial: Fraud detection, credit scoring • Sales: Customer preferences, inventory • Science: NASA galaxy DB, genome analysis

  9. AI “Application” Areas (cont.) Language Processing • Speech: dictation, HCI • Language: Machine Translation • ML & NLP: Fact Extraction • ML & words: Information Retrieval Robotics • Machine Vision • Mobile Robots & “agents” • Manipulation

  10. AI-Based Problem Solving State-Space <{S}, S0, {SGj}, {Oi}> S0: Initial State SG: Goal State (to achieve) Oi: Operators O: {S} => {S}

  11. AI-Based Problem Solving (cont.) State-Space Navigation • Forward Search: BFS, DFS, HS,… • Backward Search: BFS-1, Backchaining,… • Bi-Directional Search: BFS2,… • Goal Reduction: Island-S, MEA… • Transformation: {S}  {S’} • Abstraction: {S}  {SA} + MEA ({SA})… • Analogy: If Sim(P,P’) then Sol(P) Sol’(P’) • …

  12. More on the State Space • Useful Functions: • Succ(si) = {sk | oj(si) = sk} • Reachable(si) = {U{sk} | Succ *(si)} • Succ-1(si) = {sk | oj(sk) = si) • Reachable-1(si) = {U{sk} | (Succ-1)*(si)} • s-Path(sa0, san) = (sa0, sa1,…, san) …such that for all sa1 exists oj(sai) = sai+1 • o-Path(sa0, san) = (oj0, oj1,…, ojn-1) …such that for all sa1 exists oj(sai) = sai+1

  13. More on the State Space (cont.) • Useful Concepts: • Solution = o-Path(s0, sG) [or s-Path] • Cost(Solution) = cost(oj) … (often cost(oj) = 1) • P is solvable if at least one o-Path(s0, sG) exists • Solutions may be constructed forward, backward or any which way • State spaces may be finite, infinite, implicit or explicit

  14. Zero-Knowledge Search Simple Depth-First Search DFS(Scurr, Sgoal, S-queue) IF Scurr = Sgoal, SUCCESS ELSE Append(Succ(Scurr), S-queue) IF Null(S-queue), FAILURE ELSE DFS(First(S-queue), Sgoal, Trail(S-queue))

  15. Depth First Search 1 SI 2 7 3 5 8 4 … SG 6

  16. DFS (cont.) Problems with DFS • Deep (possibly infinite) rat holes  depth-bounded DFS, D = max depth • Loops: Succ(Succ(..Succ(S))) = S  Keep s-Path and always check Scurr • Non-Optimality: Other paths may be less costly  No fix here for DFS • Worst-case time complexity (O(bmax(D,d))

  17. DFS (cont.) When is DFS useful? • Very-high solution density • Satisficing vs. optimizing • Memory-limited search: O(d) space • Solution at Known-depth (then D=d)

  18. Zero Knowledge Search (cont.) Simple Breadth-First Search BFS(Scurr, Sgoal, S-queue) IF Scurr = Sgoal, SUCCESS ELSE Append(Succ(Scurr), S-queue) IF Null(S-queue), FAILURE ELSE BFS(Last(S-queue), Sgoal, All-But-Last(S-queue))

  19. Breadth-First Search 1 2 3 4 5 6 7 8 9 10 11 12 … SG

  20. Simple BFS cont. • Problems with BFS: • Loops: Succ(Succ(…Succ(S)))=S Pseudo-loops: Revisiting old states off-path  Keep full visited prefix tree • Worst case time complexity O(bd) • Worst case space complexity O(bd) • When is BFS Useful? • Guarantee shortest path • Very sparse solution space (better if some solution is close to SI)

  21. Zero Knowledge Search (cont.) Backwards Breadth-First Search BFS(Scurr, Sinit, S-queue) IF Scurr = Sinit, SUCCESS ELSE Append(Succ-1(Scurr), S-queue) IF Null(S-queue), FAILURE ELSE BFS(Last(S-queue), Sinit, All-But-Last(S-queue))

  22. Backwards Breadth-First Search 9 SI … 4 5 6 7 8 2 3 1 SG

  23. Backward-BFS (cont.) • Problems with Backward-BFS • All the ones for BFS • Succ(Scurr) must be invertible: Succ-1(Scurr) • When is Backward-BFS useful? • In general, same as BFS • If backward branching<forward branching

  24. Bi-Directional Search • Algorithm: • Initialize Fboundary:= {Sinit} • Initialize Bboundary:= {Sgoal} • Initialize treef:= Sinit • Initialize treeb:= Sgoal • For every Sf in Fboundary IF Succ(Sf) intersects Bboundary THEN return APPEND(Path(treef), Path-1(treeb)) ELSE Replace Sf by Succ(Sf) & UPDATE (treef) 6. For every Sb in Bboundary IF Succ(Sb) intersects Fboundary THEN return APPEND(Path(treef), Path-1(treeb)) ELSE Replace Sb by Succ-1(Sb) & UPDATE (treeb) 7. o to 5. Note: where’s the bug?

  25. Bi-Directional Breadth-First Search 1 SI 3 4 8 9 10 11 12 13 … 5 6 7 2 SG

  26. Bi-Directional Search (cont.) Problems with Bi-BFS • Loops: Succ(Succ(…Succ(S))) = S Loops: Succ-1(Succ-1(… Succ-1(S)))) = S Pseudo-loops: Revisiting old states off-path  Keep full visited prefix treef, trees • Succ(Scurr)must be invertible: Succ-1(Scurr) When is Bi-BFS useful? • Space and time complexity: O(bfd/2) + O(bbd/2) = O(bd/2) if bf = bb

  27. Island-Driven BFS • Definition: An island is a state known a-priori to be on the solution path between Sinit and Sgoal. • If there are k sequential islands: BFS(Sinit, S-(goal)= APPEND(BFS(Sinit, Sk1), BFS(Sk1, Sk2),…BFS(SIk, Sgoal)) • Upper bound complexity: O(k*maxi=0:k[bdki,ki+1]) • Complexity if islands are evenly spaced: O((k+1)*bd/(k+1))

  28. Island-Driven Search 1 SI … SIsland SG

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