Mimicking the Behaviour of Idiotypic AIS Robot Controllers Using Probabilistic Systems

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.Comment: 7 pages, 2 figures, 6 tables, 13th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2009, Orlando, Florida, US

Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance

Randomly Evolving Idiotypic Networks: Structural Properties and Architecture

We consider a minimalistic dynamic model of the idiotypic network of B-lymphocytes. A network node represents a population of B-lymphocytes of the same specificity (idiotype), which is encoded by a bitstring. The links of the network connect nodes with complementary and nearly complementary bitstrings, allowing for a few mismatches. A node is occupied if a lymphocyte clone of the corresponding idiotype exists, otherwise it is empty. There is a continuous influx of new B-lymphocytes of random idiotype from the bone marrow. B-lymphocytes are stimulated by cross-linking their receptors with complementary structures. If there are too many complementary structures, steric hindrance prevents cross-linking. Stimulated cells proliferate and secrete antibodies of the same idiotype as their receptors, unstimulated lymphocytes die. Depending on few parameters, the autonomous system evolves randomly towards patterns of highly organized architecture, where the nodes can be classified into groups according to their statistical properties. We observe and describe analytically the building principles of these patterns, which allow to calculate number and size of the node groups and the number of links between them. The architecture of all patterns observed so far in simulations can be explained this way. A tool for real-time pattern identification is proposed.Comment: 19 pages, 15 figures, 4 table

Revisiting idiotypic immune networks

SCOPUS: cp.pinfo:eu-repo/semantics/publishe

A Concentration-based Artificial Immune Network For Combinatorial Optimization

Diversity maintenance is an important aspect in population-based metaheuristics for optimization, as it tends to allow a better exploration of the search space, thus reducing the susceptibility to local optima in multimodal optimization problems. In this context, metaheuristics based on the Artificial Immune System (AIS) framework, especially those inspired by the Immune Network theory, are known to be capable of stimulating the generation of diverse sets of solutions for a given problem, even though generally implementing very simple mechanisms to control the dynamics of the network. To increase such diversity maintenance capability even further, a new immune-inspired algorithm was recently proposed, which adopted a novel concentration-based model of immune network. This new algorithm, named cob-aiNet (Concentration-based Artificial Immune Network), was originally developed to solve real-parameter single-objective optimization problems, and it was later extended (with cob-aiNet[MO]) to deal with real-parameter multi-objective optimization. Given that both cob-aiNet and cob-aiNet[MO] obtained competitive results when compared to state-of-the-art algorithms for continuous optimization and also presented significantly improved diversity maintenance mechanisms, in this work the same concentration-based paradigm was further explored, in an extension of such algorithms to deal with single-objective combinatorial optimization problems. This new algorithm, named cob-aiNet[C], was evaluated here in a series of experiments based on four Traveling Salesman Problems (TSPs), in which it was verified not only the diversity maintenance capabilities of the algorithm, but also its overall optimization performance. © 2011 IEEE.12421249De Castro, L.N., (2006) Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications, Ser. Chapman & Hall/CRC Computer & Information Science Series, , Chapman & Hall/CRCDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer VerlagJerne, N.K., Towards a network theory of the immune system (1974) Annales d'immunologie, 125 (1-2), pp. 373-389De França, F.O., Coelho, G.P., Castro, P.A.D., Von Zuben, F.J., Conceptual and practical aspects of the aiNet family of algorithms (2010) International Journal of Natural Computing Research, 1 (1), pp. 1-35De França, F.O., Coelho, G.P., Von Zuben, F.J., On the diversity mechanisms of opt-aiNet: A comparative study with fitness sharing (2010) Proc. of the 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 3523-3530Coelho, G.P., Von Zuben, F.J., A concentration-based artificial immune network for continuous optimization (2010) Proc. of the 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 108-115Coelho, G.P., Von Zuben, F.J., A concentration-based artificial immune network for multi-objective optimization (2011) Proc. of the 6th. International Conference on Evolutionary Multi-Criterion Optimization (EMO), Ser. Lecture Notes in Computer Science, 6576, pp. 343-357. , Springer Berlin/HeidelbergApplegate, D.L., Bixby, R.E., Chvátal, V., (2006) The Traveling Salesman Problem: A Computational Study, Ser. Princeton Series in Applied Mathematics, , W. J. Cook, Princeton University PressLawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G., Shmoys, D.B., (1985) The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, , ser. Wiley-Interscience series in discrete mathematics and optimization. WileyTSPLIB - A Traveling Salesman Problem Library, , http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95Burnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , G. I. Bell, A. S. Perelson, and G. H. Pimgley Jr, Eds. Marcel Dekker IncBersini, H., Revisiting Idiotypic Immune Networks (2003) Lecture Notes in Computer Science, (2801), pp. 164-174. , Advances in Artificial LifeBersini, H., Self-assertion vs self-recognition: A tribute to Francisco Varela (2002) Proc. of the 1st International Conference on Artificial Immune Systems (ICARIS), pp. 107-112Lin, S., Kernighan, B.W., An effective heuristic algorithm for the traveling-salesman problem (1973) Operations Research, 21 (2), pp. 498-516De Franca, F.O., Gomes, L.C.T., De Castro, L.N., Von Zuben, F.J., Handling time-varying TSP instances (2006) 2006 IEEE Congress on Evolutionary Computation, CEC 2006, pp. 2830-2837. , 1688664, 2006 IEEE Congress on Evolutionary Computation, CEC 2006Prokopec, A., Marin, G., Adaptive mutation operator cycling (2009) Proc. of the 2nd Intl. Conference on the Applications of Digital Information and Web Technologies (ICADIWT), pp. 661-66

A Michigan-like Immune-inspired Framework For Performing Independent Component Analysis Over Galois Fields Of Prime Order

In this work, we present a novel bioinspired framework for performing ICA over finite (Galois) fields of prime order P. The proposal is based on a state-of-the-art immune-inspired algorithm, the cob-aiNet[C], which is employed to solve a combinatorial optimization problem - associated with a minimal entropy configuration - adopting a Michigan-like population structure. The simulation results reveal that the strategy is capable of reaching a performance similar to that of standard methods for lower-dimensional instances with the advantage of also handling scenarios with an elevated number of sources. © 2013 Elsevier B.V.96PART B153163Comon, P., Jutten, C., (2010) Handbook of Blind Source Separation, , Academic PressHotelling, H., Analysis of a complex of statistical variables into principal components (1933) Journal of Educational Psychology, 24 (6), pp. 417-441Yeredor, A., ICA in boolean XOR mixtures (2007) ICA 2007 - Independent Component Analysis and Signal Separation, pp. 827-835. , SpringerGutch, H.W., Gruber, P., Theis, F.J., ICA over finite fields (2010) ICA 2010 - Latent Variable Analysis and Signal Separation, Springer, pp. 645-652Adleman, L.M., Molecular computation of solutions to combinatorial problems (1994) Science, 266 (5187), pp. 1021-1024Lidl, R., Niederreiter, H., (1997) Finite Fields, 20 VOL.. , Cambridge University PressWaterhouse, W.C., How often do determinants over finite fields vanish? (1987) Discrete Mathematics, 65 (1), pp. 103-104Coelho, G.P., De Franca, F.O., Von Zuben, F.J., A concentration-based artificial immune network for combinatorial optimization (2011) 2011 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1242-1249Back, T., Fogel, D.B., Michalewicz, Z., (1997) Handbook of Evolutionary Computation, , IOP Publishing LtdGutch, H.W., Gruber, P., Yeredor, A., Theis, F.J., ICA over finite fields - Separability and algorithms (2012) Signal Processing, 92 (2), pp. 1796-1808Silva, D.G., Attux, R., Nadalin, E.Z., Duarte, L.T., Suyama, R., An immune-inspired information-theoretic approach to the problem of ICA over a Galois field (2011) Information Theory Workshop (ITW), IEEE, pp. 618-622Shannon, C., A mathematical theory of communication (1948) The Bell System Technical Journal, 27 (3), pp. 379-423. , 623-656Yeredor, A., Independent component analysis over Galois fields of prime order (2011) IEEE Transactions on Information Theory, 57 (8), pp. 5342-5359Delfosse, N., Loubaton, P., Adaptive blind separation of independent sources a deflation approach (1995) Signal Processing, 45 (1), pp. 59-83Dietrich, F.A., (2008) Robust Signal Processing for Wireless Communications, 2 VOL.. , SpringerDe Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (13), pp. 239-251Coelho, G.P., Von Zuben, F.J., A concentration-based artificial immune network for continuous optimization (2010) 2010 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1-8De Castro, L.N., (2006) Fundamentals of Natural Computing Basic Concepts, Algorithms, and Applications, , Chapman & Hall/CRCHolland, J.H., (1992) Adaptation in Natural and Artificial Systems, , MIT PressRudolph, G., Convergence analysis of canonical genetic algorithms (1994) IEEE Transactions on Neural Networks, 5 (1), pp. 96-101Rudolph, G., Convergence of evolutionary algorithms in general search spaces (1996) Proceedings of IEEE International Conference on Evolutionary Computation, IEEE, pp. 50-54Neumann, F., Witt, C., (2010) Bioinspired Computation in Combinatorial Optimization Algorithms and Their Computational Complexity, , SpringerDe França, F.O., Coelho, G.P., Von Zuben, F.J., On the diversity mechanisms of opt-ainet: Acomparative study with fitness sharing (2010) 2010 IEEE Congress on Evolutionary Computation (CEC), IEEE, pp. 1-8De Castro, L.N., Timmis, J., (2002) Artificial Immune Systems A New Computational Intelligence Approach, , SpringerBurnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , G.I. Bell, A.S. Perelson, G.H. Pimbley, Marcel Dekker IncJerne, N.K., Towards a network theory of the immune system (1974) Annales d'Immunologie, 125 (12), pp. 373-389Bersini, H., Revisiting idiotypic immune networks (2003) Proceedings of the 7th European Conference on Advances in Artificial Life (ECAL), pp. 164-174Carlton, A.G., On the bias of information estimates (1969) Psychological Bulletin, 71 (2), pp. 108-109Schürmann, T., Bias analysis in entropy estimation (2004) Journal of Physics A Mathematical and General, 37 (27), p. 295Principe, J.C., (2010) Information Theoretic Learning Renyi's Entropy and Kernel Perspectives, , SpringerEiben, A.E., Smith, J.E., (2003) Introduction to Evolutionary Computing, , SpringerCover, T.M., Thomas, J.A., (2006) Elements of Information Theory, , 2nd ed. Wiley-InterscienceYeredor, A., (2011) MATLAB Code for ICA over GF(P), , http://www.eng.tau.ac.il/~arie/ICA4GFP.rar, AMERICA and MEXIC