1. Active Sonar Target Identification Using Evolutionary Neural Logic Networks Athanasios Tsakonas Dept. of Financial and Management Engineering, University of the Aegean, Greece Georgios Dounias Dept. of Financial and Management Engineering, University of the Aegean, Greece Nikitas Nikitakos Dept. of Shipping, Trade and Finance, University of the Aegean, Greece Presenting Author: Emmanouil Vasilakis, University of the Aegean, Greece

2. NeSy-2006, Riva Del Garda Contents Neural-Symbolic Systems and Integration Neural Logic Networks Expressing NLNs into PROLOG rules Constructing NLNs from data Past approaches The Evolutionary NLNs Applications Summary

3. NeSy-2006, Riva Del Garda Neural-Symbolic Systems and Integration Neural – Symbolic Integration Unification systemsHybrid Systems Neuronal Modeling / Neuroscience Connectionist Logic Systems Hybrid Systems by Translation Hybrid Systems by Function Source: D’Avila Garcez (2002)

4. NeSy-2006, Riva Del Garda Neural Logic Networks Finite directed graph Consisted by a set of input nodes and an output node The possible value for a node can be one of three ordered pair activation values (1,0) for true, (0,1) for false and (0,0) for don't know

5. NeSy-2006, Riva Del Garda Expressing NLNs into PROLOG rules We may create rules into the programming language PROLOG directly by every neural logic network.

6. NeSy-2006, Riva Del Garda Constructing NLNs from data : Past approaches Tan et al. 1996 Teh 1995

7. NeSy-2006, Riva Del Garda Constructing NLNs from data : Past approaches Chia and Tan 2001

8. NeSy-2006, Riva Del Garda Constructing NLNs from data : the Evolutionary NLNs

9. NeSy-2006, Riva Del Garda Active Sonar Identification 86.27 % in test set (CNLN (P1 (P1 (P1 (S1 (S1 (In T10) (Rule 0 0) E) (Rule 0 0) (S2 E (Rule 0 0) E)) (P1 (S1 (In T4) (Rule 0 0) (P2 E (Rule 10 3) (S2 E (Rule 10 3) E))) (In T11))) (P1 (P1 (In T3) (S1 (In T48) (Rule 12 8) E)) (P1 (P1 (P1 (S1 (S1 (In T10) (Rule 0 0) E) (Rule 0 0) (S2 E (Rule 0 0) E)) (P1 (S1 (S1 (In T4) (Link 50 0 (Rule 0 0)) E) (Rule 10 3) E) (P1 (S1 (In T4) (Rule 12 8) (P2 E (Rule 10 3) (S2 E (Rule 0 0) E))) (In T11)))) (P1 (P1 (In T58) (S1 (In T24) (Rule 12 8) (P2 E (Link 133 0 (Rule 0 0)) E))) (P1 (P1 (S1 (In T52) (Rule 0 0) E) (P1 (S1 (In T4) (Link 133 0 (Rule 10 3)) (P2 E (Rule 0 0) (S2 E (Rule 0 0) E))) (P1 (S1 (In T28) (Rule 0 0) (P2 E (Rule 10 3) (S2 E (Rule 0 0) E))) (In T11)))) (P1 (P1 (In T58) (S1 (In T24) (Rule 12 8) (P2 E (Link 133 0 (Rule 0 0)) E))) (P1 (P1 (S1 (In T31) (Rule 10 3) E) (In T49)) (In T42)))))) (In T4)))) (In T50)) (Rule 2 8))

10. NeSy-2006, Riva Del Garda Summary Neural-Symbolic Integration Neural Logic Network We proposed an evolutionary technique that uses: Cellular encoding Genetic programming Grammar-based search guidance Results – Application in Active Sonar Identification