Classifier Systems Anil Shankar Dept. of Computer Science University of Nevada, Reno

Anil Shankar Classifier Systems2 Overview Introduction and problem overview Architecture Component details Track a specific example Summary

Anil Shankar Classifier Systems3 Introduction Learning –“ A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E ” –Machine Learning, Tom Mitchell

Anil Shankar Classifier Systems4 RuleAddressSignal # # # 0 0 0:000 # # 0 # 0 1:010 # 0 # # 1 0:020 0 # # # 1 1:030 # # # 1 0 0:101 # # 1 # 0 1:111 # 1 # # 1 0:121 1 # # # 1 1:131 Perfect Rule Set Problem Multiplexer Example

Anil Shankar Classifier Systems5 Classifier System (C.S) 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

Anil Shankar Classifier Systems6 Rule and Message System Production system Fixed size representation for rules Parallel activation Rating of a rule by an information-based economy ::= { 0, 1} l ::= : ::={0, 1, #} l

Anil Shankar Classifier Systems7 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

Anil Shankar Classifier Systems8 Which classifier to choose?(contd…) Notation –S = Strength –P = Payment –T = Tax –R = Reward –C bid = Bid Coefficient The ith classifier strength (at time step t ) S i (t+1) = S i (t) – P i (t) – T i (t) + R i (t) Bid B i = C bid * S i Tax Tax i = C tax * S i Effective Bid EBid i = B i + N (σ bid ) In terms of strength S(t+1) = S(t) – C bid *S(t) – C tax *S(t) + R(t)

Anil Shankar Classifier Systems9 Generating better rules Bucket brigade algorithm evaluates rules and decides among competing alternatives. Use a Genetic Algorithm (GA) to generate new rules 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, # }

Anil Shankar Classifier Systems10 Generating better rules Selection is performed using roulette- wheel selection The GA is run according every GA Period or when conditioned on particular events (lack of match or poor performance)

Anil Shankar Classifier Systems11 C.S in action (1) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 0 Strength (S) Messages (Msg) Match (M) Bid (B) IndexSMsgMB 1200E E ## 0000 C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems12 C.S in action (2) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 1 Strength (S) Messages (Msg) Match (M) Bid (B) IndexSMsgMB E 00#0## C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems13 C.S in action (3) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 2 Strength (S) Messages (Msg) Match (M) Bid (B) IndexSMsgMB E20 C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems14 C.S in action (4) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 3 Strength (S) Messages (Msg) Match (M) Bid (B) IndexSMsgMB E20 C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems15 C.S in action (5) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 4 Strength (S) Messages (Msg) Match (M) Bid (B) IndexSMsgMB E20 C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems16 C.S in action (6) IndexClassifier 101## : #0 : ## : ##00 : 0001 Environment (E) 0111 T= 5 Strength (S) IndexSPayoff E20 C Bid = 0.1 C Tax = 0.0

Anil Shankar Classifier Systems17 Are these rule-sets the same? Rule # # # 0 0 0:0 # # 0 # 0 1:0 # 0 # # 1 0:0 0 # # # 1 1:0 # # # # # # :1 RuleAddressSignal # # # 0 0 0:000 # # 0 # 0 1:010 # 0 # # 1 0:020 0 # # # 1 1:030 # # # 1 0 0:101 # # 1 # 0 1:111 # 1 # # 1 0:121 1 # # # 1 1:131

Anil Shankar Classifier Systems18 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 # # # # # 0 # # 0 # # # # # # # # 1

Anil Shankar Classifier Systems19 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

Anil Shankar Classifier Systems20 Thank You Questions ?