Overview of Artificial Intelligence - PowerPoint PPT Presentation

Loading...

PPT – Overview of Artificial Intelligence PowerPoint presentation | free to download - id: 8a0bc-MTQzZ



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Overview of Artificial Intelligence

Description:

Strong AI hypothesis: Is acting intelligently sufficient? ... AI Approaches to Software Engineering* CPSC 631 Agents/Programming Environments for AI. CPSC ... – PowerPoint PPT presentation

Number of Views:57
Avg rating:3.0/5.0
Slides: 25
Provided by: thomasr1
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Overview of Artificial Intelligence


1
Overview of Artificial Intelligence
  • Thomas R. Ioerger
  • Associate Professor
  • Department of Computer Science
  • Texas AM 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
  • statesboards, operatormoves
  • symbolic integration
  • statesequations, opersalgebraic manipulations
  • class schedule
  • statespartial schedule, opersadd/remove class
  • rock band tour (traveling salesman problem)
  • statesorder of cities to visit, opersswap order
  • robot-motion planning
  • statesrobot configuration, opersjoint 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
4
3
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)
  • statepartial 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
  • Variables WA, NT, Q, NSW, V, SA, T
  • Domains Di 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(flou
r) 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(flou
r) 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) lt baked(cake)have(frosting) lt...
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
Inference Algorithms
D
AB?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
About PowerShow.com