FCAC, University of Hyderabad 1 / 30 Rules Chapter 3 Paul Thagard (2005). Mind: An Introduction to...

FCAC, University of Hyderabad

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RulesChapter 3

Paul Thagard (2005). Mind: An Introduction to Cognitive Science. 2nd Edition. MIT Press


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Outline• Rules as Mental Representations• Rules: introduction• Rule representation• Evaluation of Rules as a Representation scheme

– Representational power– Computational power

• Problem Solving: Planning, Decision, Explanation• Learning• Language

– Psychological Plausibility– Neurological Plausibility– Practical Applicability


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Introduction• Rules are similar to conditionals in Logic• Unlike Logic-based computational models, rule-

based models have all along been developed as models of mind (intelligent systems) or Cognitive Architectures– Logic Theorist (Newell, Shaw, Simon, 1958) did proofs

in formal logic to mimic human problem solving– A general version, GPS (Newell and Simon, 1972),

used rules to simulate human solutions to puzzles such as Cryptarithmetic, etc.

– ACT and ACT-R (Anderson, 1983, 1993)– SOAR (Newell 1990; Rosenbloom, Laird and Newell,

1993).


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Rules

If-then structures. E.g., If you pass 40 courses, then you will graduate with a B.A.

If – Context Then – Action

Three Part Cycle

Matching

Conflict Resolution

Action


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Rule-Based Production System

• Rule Base – Knowledge; collection of if-then rules (database)

• Context – Current state of the system; the facts known so far (state being updated)

• Interpreter – Applies rules to context (active program)


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Serial vs. Parallel Search

Serial Search – Look at first item. If not target, then move onto the next item.

Parallel Search – Multiple centers of attention. Whichever finds target cancels the search.


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Physical Symbol System

A physical device that contains a set of interpretable and combinable items (symbols) and a set of processes that can operate on the items.

See Newell’s Turing Lecture


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Commitment to Symbols

Commitment to the existence of a computational symbol-manipulating regime at the level of description most appropriate to understanding the device as a cognitive engine.


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Physical Symbol System Hypotheses (PSH)

A physical symbol system has the necessary and sufficient means for intelligent action. Being a physical-symbol system (PSS) is sufficient and necessary for intelligence.


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Problems with PSH

Consciousness

- Searle’s brain replacement - Searle’s Chinese room argument- Block’s population of China example

“C” is Fast, fluid, everyday coping activity.


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SOAR

Stores long-term knowledge symbolically

Depicts intelligence as the ability to search a symbolic problem-space.

Intelligence resides at or close to level of deliberative thought.

Intelligence consists in the retrieval of symbolically stored information and its use in the process of search.


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SOAR

Newell, 1996


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LEABRA (connectionist)

Randall O’Reilly


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ACT-R 5.0

Anderson et al., 2004


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ACT-R 5.0

Anderson et al., 2004


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What’s ACT-R used for?


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Representational Power

• Rules can be used to represent many kinds of knowledge• General information about the world (what)

• Ex: If x is a student, then x is overworked.

• Information about how to do things• Ex: If you register early, then you can get what you want

• Linguistic Regularities• Ex: If an English sentence has a plural subject, then it has a

plural verb.

• Rules of inference such as Modus Ponens

• Multiple conditions and multiple actions in a single rule


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Turing MachinesImaginary device consisting of an infinite tape, a simple processor (a finite state machine) and a read/write head. Turing machines are general purpose computational devices which rule-based systems and models of human thinking?Tape – Data storageProcessor (finite state automaton) – Remembers what state the computer is in and what symbol was just put in.Read/Write Head - Read a symbol off the tape, move itself one square forward or one square backward, and write on the tape


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Representational Power• Rules vs Logic:

• Rules are not definitional

• Rules can represent strategic information about what to do in the real world

• Actions-part can be interpreted as goals for problem solving

• Heuristics and probability can be incorporated into Rule-based systems


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Computational Power• In Logic, fundamental operation of thinking is

logical deduction• In Rules, the fundamental operation is search

– Navigate (search) through space of possibilities– Formal Search techniques

• Blind or Exhaustive: BFS, DFS– Combinatorial explosion

• Informed or Heurisitic: Best first search, A* search

• Problem solving, learning, language can all be described in terms of rule-based heuristic search through a complex space of possibilities.– LTM versus STM or WM for problem solving– Serial versus Parallel processing and their relation to

Conscious vs Unconscious processing


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Computational Power (contd.)

• Planning: How to accomplish a goal from an initial or current state?– Search through space of possibilities

• Forward, backward, bi-directional search

• Heuristic search

• Rule-based reasoning (RBR) or planning appears a lot like Logical deduction but differs from it:– In RBR, attention paid to strategies for

applying the right rule at the right time


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• Decision: Selection of best means to get to the goal.

• Difficult resolve between competing rules– Conflict Resolution needs to be supplemented

along with the rule-base


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• Explanation: In this, you are trying to understand why something happened.– Sequence of rules that fire allows you to generate

what needs to be explained from what you already know.


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• Learning: Ability to use experience to improve performance.

• Where do rules come from?– Innate:

• In-born Reflexes: If you touch hot thing, then retract the limb.

• Universal Grammar (rules) of Chomsky for Language

– Learned or acquired

• Most of learning can be described as acquisition, modification and application of rules.


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• How are rules learned?– By inductive generalization

– By other rules

• Chunking or compressing into meta-rules as in SOAR

• Combining rules (composition) as in ACT models

– By specialization

– Can be applied abductively

– Can be quantitatively evaluated based on their performance (incremental learning)


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Computational Power (contd.)• Language: How do rules underlie our ability

to use language?– Behaviorists claim we learn words through

association and repeated exposure– 1957 – Noam Chomsky publishes “Syntactic Structures.”– Argues that behaviorist learning models cannot account

for the generality of language (can understand indefinite number of sentence constructions, children’s acquisition of grammar rules, etc.).

– We are all born with an innate universal grammar.– “Rethinking innateness” book proposes competing view

point based on connectionist architecture, where rules and other things are learned by associations in the weights among nodes in a connectionist network.


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Psychological Plausibility• Psychological plausibility assessed through

mostly computer simulations (not experiments)– SOAR solves cyptarithmetic problems: Ex. DONALD +

GERALD = ROBERT – SOAR accounts for power law of practice, the rate of learning

slows down as more is learned. As you develop chunks in the initial stage, accuracy and speed improves but ceiling or saturation effect takes place wherein the higher-level chunks account for ost behavior and no new learning is necessary. Example skill-based tasks: mirror writing, typing

– Children seem to exhibit rule learning beahavior. Ex. Learning to play tic-tac-toe as studied by Crowley and Seigler (1993)

– ACT rule-based system has been applied to language learning, geometry problem solving, and computer programming


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Neurological Plausibility

• Crude analogy to neurons– If one neuron fires, then the one connected to it

fires

• ACT system components and their brain equivalents have been sketched out and fMRI expts are used to confirm.– Production rules in Basal Ganglia– Working memory of current rules in a buffer in

prefrontal cortex


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Practical Applicability• Rules are very important in education

– Rule-based systems such as ACT are used for understanding and modeling learner’s performance and also in building computer tutors that can help them learn.

• Engineering design– SOAR used for testing computer program

– SOAR model used in games like Quake to build opponents to that behave like humans

– Expert systems in industry are mostly rule-based systems

• Chemical process control, making credit decisions, diagnosis of mechanical devices.


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References

• Paul Thagard (2005). Mind: An Introduction to Cognitive Science. 2nd Edition. MIT Press.

• Slides from Jennifer Susse, Dept of Philosophy, Michigan State University (Intro to Cog Sci Course 463)

• ACT website: http://act.psy.cmu.edu

• SOAR website: http://sitemaker.umich.edu/soar

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