Problem Classical Solving
Knowledge Systems Engineering
CS 7613 Spring 2002
Administrivia
Class photo Mailing list? Laptop policy
Today
Skills survey results Getting and loading the BPS code Homework 0 Classical Problem Solvers
2.80 2.60 2.40 2.20 2.00 1.80 1.60 1.40 1.20 1.00 Symbols in
Skills Survey Results
List Writing and Comments IF, COND, CONSP, DO, Area DEFUN with List Writing and Funcall and LISP Global Structs Object Macros Area
Skills Survey Results
2.00
1.80
1.60
1.40 Series1
1.20
1.00
0.80
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Skills Survey Results
Survey averages
3.5 3 2.5 2 Series1 1.5 1 0.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13
Plans based on the survey
Lisp Tutorial Slower pace Lisp Fridays
hour on special topic, rest open
Todays focus: How to get started, how to get help
Running the BPS code
BPS-init.lsp: Defsys loader for all the BPS code well be using
Set the BPS path!
(load c:\\bps\\bps-init.lsp) (bps-menu)
Checklist (for demo)
Loading the right ACL image Setting the package to CL-USER Editing and loading a Lisp source file Header format (suggested) Setting the BPS path global. Help system Apropos and describe Making log files Defsystem files
Last time
AI programming versus conventional programming
Emphasis on knowledge representation
Overview of systems
Themes for AI Software Engineering
Problem-solvers see the world in terms of problem spaces Describe the problem in its own terms, and let Lisp make up the difference
What Classical Problem Solvers Do
Components of a Problem Space
States (conditions of the world)
Initial state (where we are) Goal state (where we want to be)
Operators (actions that can change the current state) Problem solving
Construct a path of states, using available operators, from initial to goal state
Problem-solvers see the world in terms of problem spaces
Sample Problem Spaces
Subway travel
Initial state: Davis Street Goal state: Morse Street Operators: Take purple line, take red line
Chess
Initial state: Board setup Goal state: Checkmate condition Operators: Chess moves
Problem-solvers see the world in terms of problem spaces
How to characterize a problem space
Characterizing states
Often symbols or propositional expressions
Comparing states Choosing operators
Operator applicability (can we use it?) Operator preference (is it the best one?)
Problem-solvers see the world in terms of problem spaces
Search strategy
When do we backtrack? Breadth or depth-first? Use means-end analysis?
Methods for choosing operators
When is an operator applicable?
Preconditions
Which applicable operator is best?
First available
May assume that operators written in order of preference (e.g., travel ops ordered by speed)
Problem-solvers see the world in terms of problem spaces
Random
Choose one--if it doesnt work out, try other.
Distance metric
Pick a metric that tells us which is best
Why study the classical method of problem solving?
Historically early method in AI
GPS (Newell & Simon, 1963) STRIPS (Fikes & Nilsson, 1971)
Descendants still viable: SOAR, Deep Blue Problem-solvers Provides a vocabulary for discussing see the world in terms of problem solving problem spaces
States, operators, and problem spaces
Classical Problem Solving
Separation between the search method and the problem space
Search method: generic search methods Problem space: representation of problem as set of states and operators
Search Engine
Problem Space
How Problem Spaces provide modularity and explicitness
Programming Knowledge
Search Engine
Knowledge Engine
Reasoning of the domain Representation of Domain Knowledge
Computer Program Problem Space Representation
Basic search methods
Breadth-first
Try all applicable operators before choosing secondary operators
Depth-first
On the operator chosen, continue searching until either goal is reached or search fails
Best-first
Use a distance metric to choose best operator each time
Review
Problem spaces: states, ops, and control knowledge Problem space separate from search engine How do we build it?
Building a Classical Problem Solver
Building CPS
Need a problem space API between a problem space and the search engine Requirements:
Way to describe basic characteristics of states and operators in problem space Should provide multiple, interchangable search engines Should work with novel problem spaces
Describe the problem in its own terms, and let Lisp make up the difference.
Constructing a pluggable classical problem solver
Whats a pluggable data structure? Why build a pluggable problem space obj?
Search engines, to be general, need a consistent API for the problem space No guarantee that all problem spaces will respect that API Use a pluggable class, called Problem, which mediates between the two and allow an arbitrary problem space to be used with many different search engines.
Describe the problem in its own terms, and let Lisp make up the difference.
PROBLEM Defstruct
Each problem object defines a particular problem space
E.g., taking train from GT to new aquarium
Requires following functions to define problem space:
Goal recognizer State identicality test Print states Applicable operators for given state Distance function
Once we have these, we can create generic search engines that will work with any problem space.
Using pluggable function slots
PROBLEM slot:
Goal-recognizer Operator-applier States-identical Path-filter Distance-remaining State-printer Solution-element-printer
Function description:
When has goal been found? Return set of applicable operators Are states the same? Remove path from possible paths? Estimate of distance to goal Print state Print solution path segment
Path object structure
Records a particular solution path in terms of intermediate states and operator...