chapter 6: applying the pssh
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Chapter 6: Applying the PSSH. Overview. Explain why ID3 and SHAKEY both count as illustrations of the heuristic search hypothesis Introduce frame problem Introduce background to the language of thought hypothesis (as an application of the PSSH). ID3 and PSSH. - PowerPoint PPT PresentationTRANSCRIPT
Chapter 6:Applying the PSSH
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Overview
• Explain why ID3 and SHAKEY both count as illustrations of the heuristic search hypothesis
• Introduce frame problem
• Introduce background to the language of thought hypothesis (as an application of the PSSH)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
ID3 and PSSH
• ID3 transforms one highly complex symbol structure (database) into another (a decision tree)
• The decision tree itself works by transforming symbol structures according to rules
• Rules are the IF. . . THEN . . . rules built into the decision tree
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Heuristic search
• Problems are solved by generating and modifying symbol structures until a solution structure is reached
• Problem-spaces are generally too large to be searched exhaustively (brute force algorithms)
• Search must be selective heuristic search rules
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
ID3 as heuristic search
• ID3 is a tool for navigating through the search space of decision trees
• The algorithm that it uses sorts through possible decision trees using measures of information gain and entropy reduction
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Basic concepts
Entropy – level of uncertainty (relative to a target attribute)
Information gain – reduction of entropy (relative to the target attribute)
The algorithm compares different classifying attributes in order to determine which has the highest information gain
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
SHAKEY and logic programming
Basic ideas
(1) states in state-spaces characterized by sets of predicate calculus formulas
(2) problems are conjectures to be proved
(3) problem-solving is finding proofs using proof-theoretic techniques and heuristic search techniques
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Turn on the light switch
1. Goal wff =
STATUS (SWITCH1, ON)
2. STRIPS solution =
{goto2 (BOX1), climbonbox (BOX 1),
Climboffbox (BOX 1)
Pushto (BOX 1, SWITCH 1)
Climbonbox (BOX 1)
Turnonlight (SWITCH 1)
GO SHAKEY!
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
The frame problem
• Applying an operator to a state in a state-space problem will affect some relations between objects in the state but not others
• How is the system to keep track of which relations have changed and which not?
• Proofs would be very complicated if there were axioms for each operator specifying the relevant changes and non-changes
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Possible framing axiom:
(on C B) & (put B table) (on C B)
Framing axiom?
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
SHAKEY and the frame problem
SHAKEY has a limited solution to the frame problem built into it
The F-rules in STRIPS have three components• Precondition formula• Delete list• Add formula
The Delete list makes explicit how the state description needs to be altered
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Example
• Pickup (x)
• Precondition formula: ONTABLE (x) & HANDEMPTY & CLEAR (x)
• Delete list:{ONTABLE (x), HANDEMPTY, CLEAR (x)}
• Add formula:
HOLDING (x)
Cognitive Science José Luis Bermúdez / Cambridge University Press 2010
Limitations of SHAKEY
• SHAKEY’s explicit solution to the frame problem works only because the microworld is so restricted
• Each operator will only have a small number of effects on the state• These can be explicitly listed
• But this won’t scale up, when we introduce other agents and a more complex environment