Classifier Systems Anil Shankar Dept of Computer Science

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

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

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

Overview • • • Introduction and problem overview Architecture Component details Track a specific example Summary Anil Shankar Classifier Systems 2

Introduction • Learning – “A computer program is said to learn from experience E

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 Systems 3

Problem Multiplexer Example Rule Address Signal # # # 0 0 0: 0 0

Problem Multiplexer Example Rule Address Signal # # # 0 0 0: 0 0 0 # # 0 1: 0 1 0 # # 1 0: 0 2 0 0 # # # 1 1: 0 3 0 # # # 1 0 0: 1 0 1 # # 1 # 0 1: 1 1 1 # # 1 0: 1 2 1 1 # # # 1 1: 1 3 1 Perfect Rule Set Anil Shankar Classifier Systems 4

Classifier System (C. S) • Learn simple string rules in an arbitrary environment •

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 Systems 5

Rule and Message System • Production system • Fixed size representation for rules •

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 Systems 6

Which classifier to choose? • Bucket Brigade Algorithm – For ranking or rating individual

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 Systems 7

Which classifier to choose? (contd…) • Notation – – – • • • S

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 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) Anil Shankar Classifier Systems 8

Generating better rules • Bucket brigade algorithm evaluates rules and decides among competing alternatives.

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 Systems 9

Generating better rules • Selection is performed using roulettewheel selection • The GA is

Generating better rules • Selection is performed using roulettewheel selection • The GA is run according every GA Period or when conditioned on particular events (lack of match or poor performance) Anil Shankar Classifier Systems 10

C. S in action (1) Index Classifier 1 01## : 0000 2 00#0 :

C. S in action (1) Index Classifier 1 01## : 0000 2 00#0 : 1100 3 T= 0 Index S 1 200 11## : 1000 2 200 4 ##00 : 0001 3 200 Environment (E) 0111 4 200 E 0 Strength (S) Messages (Msg) Match (M) Bid (B) M B E 20 0111 01## CBid = 0. 1 CTax = 0. 0 Anil Shankar Msg 0000 Classifier Systems 11

C. S in action (2) Index 1 2 3 4 Environment (E) Classifier 01##

C. S in action (2) Index 1 2 3 4 Environment (E) Classifier 01## : 0000 00#0 : 1100 11## : 1000 ##00 : 0001 0111 Strength (S) Messages (Msg) Match (M) Bid (B) T= 1 Index S Msg 1 180 0000 2 200 3 200 4 200 E 20 CBid = 0. 1 CTax = 0. 0 Anil Shankar Classifier Systems M B 1 20 00#0 ##00 1100 0001 12

C. S in action (3) Index 1 2 3 4 Environment (E) Classifier 01##

C. S in action (3) Index 1 2 3 4 Environment (E) Classifier 01## : 0000 00#0 : 1100 11## : 1000 ##00 : 0001 0111 Strength (S) Messages (Msg) Match (M) Bid (B) T= 2 Index S Msg M B 1 220 2 180 3 200 2 20 4 180 0001 2 18 E 20 1100 CBid = 0. 1 CTax = 0. 0 Anil Shankar Classifier Systems 13

C. S in action (4) Index 1 2 3 4 Environment (E) Classifier 01##

C. S in action (4) Index 1 2 3 4 Environment (E) Classifier 01## : 0000 00#0 : 1100 11## : 1000 ##00 : 0001 0111 Strength (S) Messages (Msg) Match (M) Bid (B) T= 3 Index S Msg M 1 220 2 218 3 180 1000 4 162 0001 3 E 20 B 16 CBid = 0. 1 CTax = 0. 0 Anil Shankar Classifier Systems 14

C. S in action (5) Index 1 2 3 4 Environment (E) Classifier 01##

C. S in action (5) Index 1 2 3 4 Environment (E) Classifier 01## : 0000 00#0 : 1100 11## : 1000 ##00 : 0001 0111 Strength (S) Messages (Msg) Match (M) Bid (B) T= 4 Index S 1 220 2 208 3 196 4 156 E 20 Msg M B 0001 CBid = 0. 1 CTax = 0. 0 Anil Shankar Classifier Systems 15

C. S in action (6) Index 1 2 3 4 Environment (E) Strength (S)

C. S in action (6) Index 1 2 3 4 Environment (E) Strength (S) Classifier 01## : 0000 00#0 : 1100 11## : 1000 ##00 : 0001 0111 T= 5 Index S 1 220 2 208 3 196 4 206 E 20 Payoff 50 CBid = 0. 1 CTax = 0. 0 Anil Shankar Classifier Systems 16

Are these rule-sets the same? Rule Address Signal Rule # # # 0 0

Are these rule-sets the same? Rule Address Signal Rule # # # 0 0 0: 0 0 0 # # # 0 0 0: 0 # # 0 1: 0 1 0 # # 1 0: 0 2 0 0 # # # 1 1: 0 3 0 # # 1 0: 0 # # # 1 0 0: 1 0 # # # 1 1: 0 # # 1 # 0 1: 1 1 1 # # # : 1 # # 1 0: 1 2 1 1 # # # 1 1: 1 3 1 Anil Shankar # # 0 1: 0 Classifier Systems 17

Multiplexer Example • Default Hierarchy – General rules cover general conditions and specific rules

Multiplexer Example • Default Hierarchy – General rules cover general conditions and specific rules cover exceptions – Parsimony ###000 0 ##0#01 0 #0##10 0 0###11 0 ###### 1 • Fewer rules – Enlargement of the solution set • While the problem space remains the same Anil Shankar Classifier Systems 18

Summary • A classifier is a simple string rule • Classifier System – rule-message

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 informationbased economy Anil Shankar Classifier Systems 19

Thank You Questions ? Anil Shankar Classifier Systems 20

Thank You Questions ? Anil Shankar Classifier Systems 20