Classifier Systems

Classifier Systems

Overview • Introduction and problem overview • Architecture • Component details • Track a specific example • Summary

The Learning Classifier System • Rule-based knowledge discovery and concept learning tool • Operates by means of evaluation, credit assignment, and discovery applied to a population of “chromosomes” (rules) each with a corresponding “phenotype” (outcome)

Components of a Learning Classifier System • Performance • Provides interaction between environment and rule base • Performs matching function • Reinforcement • Rewards accurate classifiers • Punishes inaccurate classifiers • Discovery • Uses the genetic algorithm to search for plausible rules

Knowledge Representation • Classifiers • IF-THEN rules • Condition=“genotype” • Action=“phenotype” • Strength metric • Encoded as bit strings or numerics • Population • Fixed size collection of classifiers

Low-level knowledge representation:The Classifier • Taxon is analogous to a condition (LHS) of an IF-THEN rule • Action bit is analogous to an action (RHS) of an IF-THEN rule • Strength is an internal fitness function

Problem Multiplexer Example Perfect Rule Set

Classifier System (C.S) • Learn simple string rules in an arbitrary environment • A classifier is a simple string rule • Components • Rule and Message System • Apportionment of credit system • Genetic Algorithm

Overview • General organization of a classifier system • performance system: rule based, message-passing, highly standardized, and highly parallel • credit assignment: bucket brigade algorithms • rule discovery: genetic algorithms

Definition of the basic elements • input interface: translate the current state of the environment into standard messages • classifiers (the rules used by the system): define the system’s procedures for processing messages • message list: contain all current messages (those generated by the input interface and those generated by satisfied rules) • output interface: translate some messages into effector actions that modify the state of the environment

Classifier Systems (2) • Basic parts of a classifier system

Execution cycle • Step 1. Add all messages from the input interface to the message list. • Step 2. Compare all messages on the message list to all conditions of all classifiers and record all matches (satisfied conditions) • Step 3. For each set of matches satisfying the condition part of some classifiers, post the message satisfied by its action part to a list of new messages. • Step 4. Replace all messages on the message list by the list of new messages. • Step 5. Translate messages on the message list to requirements on the output interface, thereby producing the system’s current output. • Step 6. Return to Step 1.

Rule and Message System • Production system • Fixed size representation for rules • Parallel activation • Rating of a rule by an information-based economy • <message>::= { 0, 1} l • <classifier>::= <condition>:<message> • <condition>::={0, 1, #}l

Which classifier to choose? • Bucket Brigade Algorithm • For ranking or rating individual classifiers • Classifiers buy and sell the right to trade information (information-based economy) • Auction house and clearing house • If a classifier matches a message, it participates in an auction. • The bid (B) is proportional to its strength (S) • Once activated the winner pays its bid to other classifiers which also matched the message

Which classifier to choose?(contd…) • Notation • S = Strength • P = Payment • T = Tax • R = Reward • Cbid = Bid Coefficient • The ith classifier strength (at time step t) Si(t+1) = Si(t) – Pi(t) – Ti(t) + Ri(t) • Bid Bi = Cbid * Si • Tax Taxi = Ctax * Si • Effective Bid EBidi = Bi + N (σbid) • In terms of strength S(t+1) = S(t) – Cbid*S(t) – Ctax*S(t) + R(t)

Generating better rules • Bucket brigade algorithm evaluates rules and decides among competing alternatives. • We could still inject new (possibly better rules), so use a Genetic Algorithm (GA) • A classifier’s strength (S) is used as its fitness • Similar to the simple genetic algorithm • Entire population is not replaced at the next generation (Generation gap ) • GA period (epoch) • Number of time steps between GA calls • Time step = rule-message cycle • Crowding to maintain diversity • Mutation over a ternary alphabet {1, 0, # }

Generating better rules • Selection is performed using roulette-wheel selection • The GA is run according to the GA Period or when conditioned on particular events (lack of match or poor performance)

T= 0 C.S in action (1) Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0

C.S in action (2) T= 1 Strength (S) Messages (Msg) Match (M) Bid (B) CBid = 0.1 CTax = 0.0

C.S in action (6) T= 5 Strength (S) CBid = 0.1 CTax = 0.0

Are these rule-sets the same?

Multiplexer Example • Default Hierarchy • General rules cover general conditions and specific rules cover exceptions • Parsimony • Fewer rules • Enlargement of the solution set • While the problem space remains the same

Summary • A classifier is a simple string rule • Classifier System • rule-message system, • apportionment of credit mechanism • GA • Advantages of CS • rules are simple • use fixed length representation • parallel activation • operate in an information-based economy

Thank You Questions ?

Classifier Systems