ŠĻą”±į>ž’ oqž’’’n’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’’ģ„ĮE@ šæ3bjbjƒęƒę %ZįŒįŒķ*0’’’’’’ˆ8888888Lš/š/š/8(0 40ŒL·JLĢ0Ģ0Ģ0Ģ0Ģ0Ģ0Ģ0Ģ0ė:ķ:ķ:ķ:C0;„“B„8J$LRUN‚\J8ł3Ģ0Ģ0ł3ł3\J88Ģ0Ģ0qJ­8­8­8ł3N8Ģ08Ģ0ė:­8ł3ė:­8"­8Ļ988Ļ9Ģ0Ą0 0zŅvØĮš/G52Ļ9÷9ō‡J0·JĻ9×Ny7×NĻ9LL8888×N8Ļ9(Ģ0Ź–1­8&2tš2_Ģ0Ģ0Ģ0\J\JLL¤,š/—8LLš/Overview Foundation Issues in Cognitive Science All Intelligent Behavior Can Be Described as Problem Solving Programs Can Be Written To Search Problem Spaces The Computational Complexity is O(bn) where b is the branching factor and n is the number of operators in the solution path For interesting problems, b and n can be large The Psychology 0f Problem Solving and Skilled Performance Common Themes Fundamental Role of Similarity Task Orientation Interactions of Basic Processes to Generate Action Learning Computer Simulation Models Artificial Intelligence Dealing with O(bn) Learning the Hard Way About Values of b and n Chess, Math, etc. b = 10 to 30, n = 10 to 40 Scene and Language Understanding, etc. b and n Much Larger Algorithms verses Heuristic Methods Heuristic Search Methods Reduce b by “ignoring” alternatives Best First Search, Means-Ends Analysis Look ahead (chess, etc) Planning by Abstraction (reduce both b and n) Problem Reduction Changes in Representation (reduce both b and n) Learning (reduce b to 1 in the limit) Problem Space Hypothesis The fundamental organizational unit of all human goal-oriented symbolic activity is the problem space. Assumption: Fundamental process underlying intelligent action is Search Alternative: Language comprehension and knowledge-based inferences PROBLEM SPACE knowledge states operators that generate new knowledge states a sequence of operators describes a path PROBLEM a set of initial states a set of goal states a set of path constraints the problem is to find a path from a start state to a goal state that satisfies the path constraints Simple Cases Tower of Hanoi Possible Disk Configuration Generated by Legal Moves Water Jug Problem Gets More Complex Quickly “Right” Representation and Insight Problems SEARCH CONTROL Decide to quit the problem Decide if a goal state has been produced Select a state from the stock to be the current state Select an operator to be the current operator Decide to save the new state just produced by an operator Operate Within the Following Cycle 1. Select a state; Select an operator 2. Apply operator to a state producing a new state 3. Decide if a goal state; decide to quit; decide to save a new state Search control depend on know that is immediately available Resource and Capacity Limits Serial Action: At most one problem space operator can be performed at one time Problem solving will consider on one move at a time In the problem space, maybe several moves in the external word Example: A move a two disk stack in the tower of Hanoi Finite Stock: The subject has a limited number of states (the stock) available to become the current state. (i.e. humans can and will only consider a limited number of alternatives) Search Control: Use only immediately available knowledge Multiple problem spaces, e.g. an operator selection space Time course of behavior: 5 to 15 second per state Grain size of analysis: very detailed in comparison to most psychological models Search Control II (Reduce b) Search Methods Generate and Test Heuristic Search: Depth, Breadth, or Best First Hill Climbing* Mean-End Analysis Operator Subgoaling Planning Evaluation Functions (Computers) Distance To Goal Likelihood that State Is On Solution Path Weighted Average of Desirable Properties … Evaluation Functions (Human) Similarity of Appearance to Goal Similarity of Meaning to Goal Knowledge that This State Is On Solution Path Means-Ends Analysis General Problem Solver Newell, Shaw, and Simon (1968) Difference Reduction Several Kinds of Differences Between Goal and Current State Ends (goals and subgoals) Set Up Goals and Subgoals to Reduce Differences Means (Operators) Operators Effect Some Differences and Not Others Table of Connections Examples Tower of Hanoi Algebra Monkey and Bananas Top Goal = (Transform the Initial-Object into the Desired-Object) Initial-Object = (Monkey’s-Place = Place-1, Box’s-Place = Place-2, Contents-of-Monkey’s-Hand = Empty) Desired-Object = (Monkey’s-Place = On-Box, Box’s-Place = Under-Bananas, Contents-of-Monkey’s-Hand = Bananas) Operators Walk, Move-Box, Climb, Get-Bananas Preconditions Difference Ordering Table of Connections Monkey-PlaceBox-PlaceMonkey-HandWalkXMove-BoxXXClimbXGet-BananasXTrace of GPS Solving Problem 1 Transform Initial-Obj into Desired-Obj 2 Reduce Contents-of-Monkey’s Hand Diff On Initial-Obj 3 Apply Get-Bananas on Initial-Obj 4 Reduce Location-of-Box Diff On Initial-Obj 5 Apply Move-Box to Under-Bananas On Initial-Obj 6 Reduce Location-of-Monkey Diff On Initial-Obj 7 Apply Monkey Walk to Location of Box on Initial-Obj (Monkey’s-Place = Place-2, Box’s-Place = Place-2, Contents-of-Monkey’s-Hand = Empty) 8 Apply Move-Box to Under-Bananas (Monkey’s-Place = Under-Bananas, Box’s-Place = Under-Bananas, Contents-of-Monkey’s-Hand = Empty) 9 Apply Get-Bananas to Current-Obj 10 Reduce Location-of-Monkey Diff 11 Apply Climb to Current-Obj 12 Apply Get-Bananas to Current-Obj 13 Transform Initial-Obj into Desired-Obj Psychology of Problem Solving Major Traditions Problem Taxonomies Theoretical and Empirical Methodologies Levels (Kinds) Of Theoretical Analyses Understanding and Search Major Research Traditions Cognitive Action Account for Complex Action Sequences General behavior theory (Thorndike, Hull, Skinner, Tolman, Staats, .) Modern cognitive theory- e.g., Rule-based models of skill acquisition (Newell, Simon, Anderson, .....) Cognitive Representation Mental Representations That Generate Action Sequences Gestalt psychology (Duncker, Katona, Kohler, Wertheimer, ...) Modern research on representation (Greeno, Kintsch, Simon, ....) Modern Research On Problem Solving Attempts to Synthesize the Two Traditions Problem Taxonomies Task Orientation How are various tasks related? Well-Structured (Closed) Problems Puzzles Instructional Problems Characteristics of ... Explicit goal Known operators Known values of b and n, often small! Ill-Structured (Open) Problems Design Real-Life” Problems Characteristics of ... b and n large or indefinite! Ill-Structured Characteristics of Well-Structured Problems (Simon) Decomposition of an Ill-Structured Problem Into A Collection of Well Structured Problems Theoretical and Empirical Methodologies Explicit Process Models Computer Simulations Production Systems (Rule-Based) Verbal Protocol Analysis Comparisons between Novices and Experts Problems designed for the instruction of novices Common task done by both experts and novices Task done by experts Problem Solving as an Arena to Test General Theories Levels (Kinds) Of Theoretical Analyses Decomposition into “Higher-Level” Processes Preparation, Insight, Creativity, Incubation, Set, Functional Fixedness, Brain Storming, ... Demonstrations of .... Process Models Rules Elementary Information Processes Description verses Explanation ... Demonstrations verses Explanations ... Continuum Hypothesis (Simon) Solution of Ill-structured problems by reduction to a collection of well-structured problems Creativity and scientific discovery can be explained using the same processes used to solve well-structured problems E.g., Insight is just a memory process (Recognition) Understanding and Search Problem Solving as Understanding (Comprehension) (Wertheimer, Hayes and Simon, Kintsch, ....) Search (Newell, Simon, AI Literature...) Problem Space Hypothesis Understanding-Search Increasing Importance of Understand Processes Multiple Problem Spaces Weak Methods In Human Generate and Test Means-Ends Analysis Difference Reduction-Similarity Operator Subgoaling Problem Decomposition (Reduction) Planning by Abstraction Difference Reduction-Similarity A Form of Hill Climbing In Many Simple Problems It Turns Into: Select moves by comparing consequences of each move from the current state with goal. Pick move that leads to state that is "closer" (more similar to ) the goal. Atwood and Polson (1976) Water Jug Problems Similarity of Descriptions Select moves by comparing descriptions of each available action from the current state with a description of the goal. Learning to use computers, phone mail, and other complex systems. Lewis and Polson (1990) Label following CoLiDeS (Kitajima, Blackmon, Polson, In Press) Atwood and Polson (1976) Perfect Example of Problem Space Hypothesis States Legal configurations of water in each jug Operators Legal pouring operations Search Control Knowledge Decide to quit the problem Quit if succeed Quit when told by the experimenter Decide if goal state has been produced Select an operator to be the current operator Prefer operators that lead to states that are similar to the goal state Prefer operators that lead to new states Do not prefer operators that lead to old states Reject operators that lead to the immediately preceding state Always select operators that lead to the goal state Select operator at random if no other basis for preferred move Brief Review of Polson and Jeffries Three Stage Move Selection Process 1. Means-Ends (Similarity, Evaluation Function) 2. New moves (Use of LTM) 3. Best move or random (STM limits) Memory Very simplified model Parameters Description of random process Simplifying assumptions Individual differences/noise in decision processes Constraints on parameter values Constant across some kinds of manipulations Vary in a “lawful” way for other kinds of manipulations Water Jugs Figure showing (8,5,3) problem Test of means-ends assumption (8,5,3) Vs (24,21,3) Goodness of fit Observed and Predicted Means and Standard Deviations (8, 5, 3) Observed Predicted Mean 24.90 23.69 StD 14.75 15.31 (24, 21, 3) Observed Predicted Mean 12.03 11.84 StD 7.44 6.66 Working Backwards Geometry Novices Solving Physics Problems Planning problems Paint the ladder and ceiling green Define new subgoals Monkey and the Bananas Trivial (for humans) Huge search space Important Problem in the Early History of AI Knowledge Use of Knowledge to: * Build Effective Representations * Control Search Search Control Basic Operations (Actions) Knowledge of Individual Steps Rule-based representation Transfer implications Strategic Knowledge Strategic Knowledge Beyond Simple Puzzle Like Problems Text Book Problems in Geometry, Algebra, Physics Ill-structured Problems like Design Interaction between problem representation and search methods Specialized knowledge required by a given search method Strategic Knowledge, Constructions, Etc. 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