Download Knowledge Representation: Rules

Knowledge Representation: Rules
Knowledge Representation
• formal logic
• rules
• concepts
• analogies
• images
• connections
Rule-based systems: example
How to get from Kalamazoo to Chicago? IF you want to get to Chicago, and you are in Kalamazoo,
and you have no car, THEN take the train.
IF you want to take the tarin from Kalamazoo to Chicago, THEN go to the train station and buy a
ticket.
IF you want to buy a ticket, THEN get some money.
IF you want to get some money, then go to the bank and withdraw it.
IF you want to get to Chicago, and you have a car, THEN take highway I94 than I90.
IF: conditions ; THEN: action
What rules do you use to plan weekend entertainment?
Rule-based systems: some historical remarks
Newell and Simon, General problem Solver: GPS 1950s-60s
well-defined problems: math. problems, chess
ill-defined problems: how to get a job, how to bring economical decisions etc...
GPS
GPS needs well-defined problem
• Representation of state space
• Set of operators (actions)
• Table of differences/operators
Search is guided by Means-end analysis
subgoals to reduce particular differences between current state and goal state
EXAMPLE
Tower of Hanoi
http://www.cut-the-knot.org/recurrence/hanoi.shtml
GPS
Newel and Simon’s main research effort was aimed at extending the boundaries of artificial intelligence,
with particular concern for the simulation of human thought processes, moving from the well-structured
tasks addressed in early A.l. programs to wider ranges of tasks that call on substantial bodies of knowledge and that are relatively loosely structured. Among the important early programs were the General
Problem Solver (GPS)
The initial idea was to represent problems of some general class as problems of transforming one
expression into another by means of a set of allowed rules. GPS was the first system to separate the
problem solving structure of goals and subgoals from the particular domain.
If GPS had worked out to be really general, perhaps the Newell and Simon predictions about rapid
success for AI would have been realized.
GPS: Limitations
• lot of the work done in the original framing
• part of the intelligence is recognizing the problem and relevant symbols
• humans operate in very many domains
• the general problem solver isn’t very general
Production Systems
• Cognitive skills are realized by production rules
• Production rules are organized around a set of goals
• Complex cognitive processes involve a sequence of production rules
• production rules are an elaborate knowledge base
• Rules are psychologically realistic, because they describe many aspects of skilled behavior, and
predict the details of that behavior
Production Systems and the ACT
”Architecture of Cognition” (John R. Anderson, CMU)
The goal is to understand how people organize knowledge that they acquire from their diverse experiences to produce intelligent behavior.
The concern is very much with how it is all put together and this has led to the focus on what are
called ”unified theories of cognition.” A unified theory is a cognitive architecture that can perform in
detail a full range of cognitive tasks.
The theory is called ACT-R and takes the form of a computer simulation which is capable of performing
and learning from the same tasks that subjects.
ACT-R is also an instance of a hybrid cognitive architecture in that it represents knowledge symbolically as rules and facts but also has a neurally-based activation process that determines which facts
and rules get deployed in which situations.
http://act-r.psy.cmu.edu/people/ja/ja-interests
SOAR
Newell and his students, 1980s, John E. Laird (Univ. Michigan)
”Unified theory of cognition”
Soar is a general cognitive architecture for developing systems that exhibit intelligent behavior.
Researchers all over the world, both from the fields of artificial intelligence and cognitive science,
are using Soar for a variety of tasks.
It has been in use since 1983, evolving through many different versions to where it is now Soar, Version
8.5.
John Laird
http://sitemaker.umich.edu/soar
ELIZA
ELIZA emulates a Rogerian psychotherapist.
ELIZA has almost no intelligence whatsoever, only tricks like string substitution and canned responses
based on keywords. Yet when the original ELIZA first appeared in the 60’s, some people actually
mistook her for human. The illusion of intelligence works best, however, if you limit your conversation
to talking about yourself and your life.
http://www.manifestation.com/neurotoys/eliza.php3
How different rule-based represenation from logic?
• Note less informational power than logic: rule-based system may not have full quantifiers and
rules of inference.
• But it can nevertheless be more computationally powerful, just because it focuses on the task to
be accomplished
• Uses processes that are not inherently part of logic: subgoaling.
Strengths of rule-based systems
• Have been used in many commercial technical systems
• Modular, easy to add to
• Have modeled various kinds of psychological experiments
• Lots of human knowledge and ability naturally described in terms of rules
Disadvantages of rules
• Natural language is much more flexible than formal logic: not easy to formalize
• Restricted to verbal information
• Much reasoning is nonmonotonic: you can’t just add more beliefs deductively, but must subtract
as well
• Potentially computationally explosive
• Most interesting kinds of reasoning are non-deductive
From Logic to Rules and Mental Models
Taxonomy of thought
A taxonomy of thought
Goal?
Yes
No
Daydream
Deterministic ?
No
Yes
Precise goal
No
Calculation
Yes
Creation
Increase
in semantic
information ?
Yes
Induction
No
Deduction
Language
Basic questions:
1. What representations are required for our ability to understand and produce language?
2. How is language learned?
Behaviorist answer:
Language is based on a set of associations, learned by trial and error.
Chomsky’s revolution
Language
Syntactic Structures 1957
Rejected associationism
• grammars are complex, rule-like structures
• universal grammar is innate
• we are born with readiness to notice what kind of grammar our native language has
Steven Pinker, ”Rules of Language”