Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bio informatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.292143154Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and genetic programming (2003) Proc. of the Congress on Evolutionary Computation (CEC 2003), pp. 1384-1391. , Canberra, IEEE PressBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, , Plenum PressBonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressDe Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group IncDeneubourg, J.L., Goss, S., Sendova-Franks, N.A., Detrain, C., Chrétien, L., The dynamics of collective sorting: Robot-like ant and ant-like robot (1991) Simulation of Adaptive Behavior: from Animals to Animats, pp. 356-365. , J. A. Meyer and S. W. Wilson (eds.). MIT Press/Bradford BooksEveritt, B.S., Landau, S., Leese, M., (2001) Cluster Analysis, , Arnold Publishers, LondonGutowitz, H., Complexity-seeking ants (1993) Proceedings of the Third European Conference on Artificial LifeHandl, J., Knowles, J., Dorigo, M., On the performance of ant-based clustering (2003) Proc. of the 3rd International Conference on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems, pp. 204-213. , IOS PressHandl, J., Meyer, B., Improved ant-based clustering and sorting in a document retrieval interface (2002) Lecture Notes in Computer Science, 2439, pp. 913-923. , J.J. Merelo, J.L.F. Villacañas, H.G. Beyer, P. Adamis Eds.: Proceedings of the PPSN VII - 7th Int. Conf. on Parallel Problem Solving from Nature, Granada, Spain, Springer-Verlag, BerlinKanade, P., Hall, L.O., Fuzzy ants as a clustering concept (2003) Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232Kaufman, L., Rousseeuw, P.J., (1990) Finding Groups in Data - An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, , John Wiley & Sons IncKeim, D.A., (2002) Information Visualization and Visual Data Mining: IEEE Transactions on Visuali Zation and Computer Graphics, 7 (1), pp. 100-107Kennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersLabroche, N., Monmarché, N., Venturini, G., A new clustering algorithm based on the chemical recognition system of ants (2002) Proc. of the 15th European Conference on Artificial Intelligence, pp. 345-349. , France, IOS PressLumer, E.D., Faieta, B., Diversity and adaptation in populations of clustering ants (1994) Proceedings of the Third International Conference on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , MIT PressMonmarché, N., Slimane, M., Venturini, G., On improving clustering in numerical databases with artificial ants. Advances in artificial life (1999) Lecture Notes in Computer Science, 1674, pp. 626-635. , D. Floreano, J.D. Nicoud, and F. Mondala Eds., Springer-Verlag, BerlinPaton, R., (1994) Computing with Biological Metaphors, , Chapman & HallRamos, V., Merelo, J.J., Self-organized stigmergic document maps: Environment as a mechanism for context learning (2002) AEB'2002, First Spanish Conference on Evolutionary and BioInspired Algorithms, pp. 284-293. , E. Alba, F. Herrera, J.J. Merelo et al. Eds., SpainRamos, V., Muge, F., Pina, P., Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds. IOS Press, AmsterdamRitter, H., Kohonen, T., Self-organizing semantic maps (1989) Biol. Cybern., 61, pp. 241-254Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Lecture Notes in Computer Science, 3316, pp. 1088-1093. , Proc. of ICONIP 2004, Special Session on Ant Colony and Multi-Agent SystemsSherafat, V., De Castro, L.N., Hruschka, E.R., The influence of pheromone and adaptive vision on the standard ant clustering algorithm (2004) Recent Developments in Biologically Inspired Computing, pp. 207-234. , L. N. de Castro and F. J. Von Zuben, Chapter IX. Idea Group IncVizine, A.L., De Castro, L.N., Gudwin, R.R., Text document classification using swarm intelligence (2005) Proc. of KIMAS 2005, , CD ROMYeung, K.Y., Medvedovic, M., Bumgarner, R.E., Clustering gene-expression data with repeated measurements (2003) Genome Biology, 4 (5), pp. R34. , articl

A review of motivational systems and emotions in cognitive architectures and systems

Motivational Systems are specific modules of Cognitive Architectures, responsible for determining the behavior of artificial agents based on cognitive models of human motivations and emotions. In this work we discuss how these ideas coming from psychology can be used in the field of cognitive architectures, explaining how motivational systems differ from other kinds of systems, and how they can be used to build control systems for artificial agents.118666584FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2013/07559-

A Cognitive Neuroscience-inspired Codelet-based Cognitive Architecture For The Control Of Artificial Creatures With Incremental Levels Of Machine Consciousness

The advantages given by machine consciousness to the control of software agents were reported to be very appealing. The main goal of this work is to develop artificial creatures, controlled by cognitive architectures, with different levels of machine consciousness. To fulfil this goal, we propose the application of cognitive neuroscience concepts to incrementally develop a cognitive architecture following the evolutionary steps taken by the animal brain. The triune brain theory proposed by MacLean and also Arrabale's ConsScale will serve as roadmaps to achieve each developmental stage, while iCub - a humanoid robot and its simulator - will serve as a platform for the experiments. A completely codelet-based system "Core" has been implemented, serving the whole architecture.4350Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y., An integrated theory of the mind (2004) Psychological Review, 111 (4), pp. 1036-1060. , DOI 10.1037/0033-295X.111.4.1036Arrabales, R., Ledezma, A., Sanchis, A., A Cognitive approach to multimodal attention (2009) Journal of Physical Agents, 3 (1), pp. 53-63Arrabales, R., Ledezma, A., Sanchis, A., Assessing and characterizing the cognitive power of machine consciousness implementations (2009) AAAI Fall Symposium Series, , 2009Arrabales, R., Ledezma, A., Sanchis, A., Towards conscious-like behavior in computer game characters (2009) Computational Intelligence and Games, 2009, CIG 2009, pp. 217-224. , IEEE Symposium on, IEEEArrabales, R., Ledezma, A., Sanchis, A., ConsScale, A pragmatic scale for measuring the level of consciousness in artificial agents (2010) Journal of Consciousness Studies, 17 (3-4), pp. 131-164. , (34)Arrabales, R., Ledezma, A., Sanchis, A., The cognitive development of machine consciousness implementations (2010) International Journal of Machine Consciousness, 2 (2), p. 213Baars, B.J., (1988) A Cognitive Theory of Consciousness, , Cambridge Univ PressBaars, B.J., Theatre of consciousness, global work space theory, a rigorous scientific theory of consciousness (1996) Journal of Consciousness Studies, 4 (1), pp. 292-309Baars, B.J., Franklin, S., How conscious experience and working memory interact (2003) Trends in Cognitive Sciences, 7 (4), pp. 166-172. , DOI 10.1016/S1364-6613(03)00056-1Baars, B.J., Franklin, S., Consciousness is computational: The lida model of global workspace theory (2009) International Journal of Machine Consciousness, 1, p. 23. , (2009Baars, B.J., Gage, N.M., (2010) Cognition, Brain, and Consciousness: Introduction to Cognitive Neuroscience, , Academic PressBaddeley, A., Working memory and language: An overview (2003) Journal of Communication Disorders, 36 (3), pp. 189-208. , DOI 10.1016/S0021-9924(03)00019-4Brooks, R.A., Intelligence without representation (1991) Artificial Intelligence, 47, pp. 139-159. , (1-31-3)Dennett, D.C., (1991) Conciousness Explained, , Back Bay BooksD'Mello, S.K., Franklin, S., Ramamurthy, U., Baars, B.J., A cognitive science based machine learning architecture (2006) AAAI 2006 Spring Symposium Series Sponsor: American Association for Artificial Intelligence, , Stanford University, Palo Alto, California, USADoerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C., Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolio selection (2004) Annals of Operations Research, 131 (1-4), pp. 79-99. , DOI 10.1023/B:ANOR.0000039513.99038.c6, Meta-Heuristics - Theory, Applications and SoftwareEdelman, D.B., Baars, B.J., Seth, A.K., Identifying hallmarks of consciousness in non-mammalian species (2005) Consciousness and Cognition, 14 (1), pp. 169-187. , DOI 10.1016/j.concog.2004.09.001, Neurobiology of Animal ConsciousnessEdelman, G.M., (2004) Wider than the Sky: The Phenomenal Gift of Consciousness, , Yale Univ PrFranklin, S., (1997) Artificial Minds, , The MIT PressFranklin, S., Baars, B.J., Ramamurthy, U., Ventura, M., The role of consciousness in memory (2005) Brains, Minds and Media, 1 (1), p. 38Franklin, S., Graesser, A., Is it an agent, or just a program? : A taxonomy for autonomous agents (1997) Intelligent Agents III Agent Theories, Architectures, and Languages, pp. 21-35Fuster, J.M., (2008) The Prefrontal Cortex, , Academic PressGoleman, D., (2006) Emotional Intelligence, , Bantam Dell Pub GroupHaikonen, P.O., (2007) Robot Brains: Circuits and Systems for Conscious Machines, , Wiley-InterscienceHaykin, S., (1999) Neural Networks: A Comprehensive Foundation, , Prentice Hall, 2 ed. ednHofstadter, D.R., Mitchell, M., The copycat project: A model of mental fluidity and analogy-making (1994) Advances in Connectionist and Neural Computation Theory, 2, pp. 31-112. , In Holyoak, K.J & Barnden, J.A. (Eds)Holland, J.H., (1992) Adaptation in Natural and Artificial Systems, , Cambridge, MA: MIT PressLaird, J.E., Extending the soar cognitive architecture (2008) Proceeding of the 2008 Conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference, pp. 224-235. , Amsterdam: IOS PressLangley, P., Laird, J.E., Rogers, S., Cognitive architectures: Research issues and challenges (2009) Cognitive Systems Research, 10 (2), pp. 141-160Maclean, P.D., Brain evolution relating to family, play, and the separation call (1985) Archives of General Psychiatry, 42 (4), p. 405Maclean, P.D., (1990) The Triune Brain in Evolution: Role in Paleocerebral Functions, , NY: SpringerMetta, G., Sandini, G., Vernon, D., Natale, L., Nori, F., The iCub humanoid robot: An open platform for research in embodied cognition (2008) Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, pp. 50-56. , ACMNegatu, A.S., Franklin, S., An action selection mechanism for conscious software agents 1 (2000) Cognitive Science Quarterly, pp. 1-21Rodriguez, F., Galvan, F., Ramos, F., Castellanos, E., Garcia, G., Covarrubias, P., A cognitive architecture based on neuroscience for the control of virtual 3D human creatures (2010) Brain Informatics: International Conference, (BI-2010), p. 328Silva, R.C.M., Gudwin, R.R., An introductory experiment with a conscious-based autonomous vehicle (2010) Proceedings of ROBOCONTROL 2010 - IV Workshop in Applied Robotics and Automation, pp. 1-9Suman, B., Kumar, P., A survey of simulated annealing as a tool for single and multiobjective optimization (2005) Journal of Oper Res Soc, 57, pp. 1143-1160Sun, R., The importance of cognitive architectures: An analysis based on CLARION (2007) Journal of Experimental and Theoretical Artificial Intelligence, 19 (2), pp. 159-193. , DOI 10.1080/09528130701191560, PII 780395031Tononi, G., Consciousness as integrated information: A provisional manifesto (2008) The Biological Bulletin, 215 (3), pp. 216-242Vernon, D., Metta, G., Sandini, G., The iCub cognitive architecture: Interactive development in a humanoid robot (2007) 2007 IEEE 6th International Conference on Development and Learning, pp. 122-12

Designing Intelligence Augmentation System With A Semiotic-oriented Software Development Process

In this paper, the authors describe the first steps of a semiotic-oriented software development process called "Brainmerge" [1], and present a case study where the process is used to coordinate the steps required for the design of a locomotive and car assignment system, modeled as an Intelligence Augmentation Systems (IAS). IAS represent an extension of Decision Support Systems (DSS) [2] with the use of Peircean semiotics [4], intelligence augmentation techniques [3], computational agents [5] and usually dynamic decision making processes (DDMP). IAS are suitable for DDMP. Traditional software methodologies are usually inefficient when dealing with IAS, and our methodology aims at fulfilling these shortcomings. This work considers the resolution of a real world problem of tactical planning in railroads where such DDMP play a key role. © 2007 IEEE.8489Paraense, A., Gudwin, R., Gonçalves, R., Brainmerge: A Semiotic-Oriented Software Development Process for Intelligence Augmentation Systems (2007) Integration of Knowledge Intensive Multi-Agent Systems KIMAS '07: Modeling, EVOLUTION and Engineering, , April 29, May 3, Waltham, MassachusettsShim, J.P., Past, present and future of decision support technology, Elsevier (2002) Decision Support Systems, 33, pp. 111-126Engelbart, D.C., AUGMENTING HUMAN INTELLECT: A Conceptual Framework Summary reportRansdell, J., The Relevance of Peircean Semiotic to Computational Intelligence Augmentation (2003) SEED Journal (Semiotics, Evolution, Energy, and Development), 3 (3)Franklin, S., Graesser, A., Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents (1996) Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages, , Budapest, Hungar

Brainmerge: A Semiotic-oriented Software Development Process For Intelligence Augmentation Systems

In this paper we present "Brainmerge", a software development process designed according to semiotic principles in order to coordinate the steps required for the requirements specification, analysis and design of a special kind of intelligent system, which is usually described as Intelligence Augmentation Systems (IAS). IAS represent an extension of Decision Support Systems (DSS) with the use of AI techniques and usually dynamic decision making processes. Traditional software methodologies are usually inefficient when dealing with IAS, and our methodology aims at fulfilling these shortcomings. To illustrate the methodology we describe a real application in planning the circulation of trains in a single track railroad. © 2007 IEEE.261266Shim, J.P., Past, present and future of decision support technology. Elsevier (2002) Decision Support Systems, 33, pp. 111-126Shim, J.P., Engelbart, D.C., AUGMENTING HUMAN INTELLECT: A Conceptual Framework Summary report, , http://sloan.stanford.edu/mousesite/ EngelbartPapers/B5_F18_ConceptFrameworkIndhtmlRansdell, Joseph, The Relevance of Peircean Semiotic to Computational Intelligence Augmentation (2003) SEED Journal (Semiotics, Evolution, Energy, and Development), 3 (3). , http://www. library.utoronto.ca/see/SEED/Vol3-3/Ransdell.ht

A Simulator Using Classifier Systems With Neural Networks For Autonomous Robot Navigation

This paper presents a simulator that was developed to assist in the process of implementing high-level autonomous robot navigation algorithms and in the related experimentations. Classifier systems are designed here, using neural networks as classifiers, to perform autonomous navigation. We propose a powerful simulator using classes and objects to be easily updated and extended. The simulator carries a class composed of methods for differential wheels steering, for detecting collision, and for sensor readings. Another class allows the specification of geometric shaped objects, which can also be detected as obstacles in the environment. In addition, operators are available to deal with credit assignment, genetic algorithms, and inference of the classifiers. By designing and constructing the simulator, we create conditions to explore the potentialities of neural networks as classifiers.1501506Ackley, D., Littman, M., Interactions between learning and evolution (1991) Artificial Life II, 10. , SFI Studies in the Sciences of Complexityedited by C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen, Addison-WesleyBooker, L.B., Goldberg, D.E., Holland, J.H., Classifier systems and genetic algorithms (1989) Artificial Intelligence, 40, pp. 235-282Goldberg, D.E., (1989) Genetic Algorithms in Search, Optimization and Machine Learning, , Addison-WesleyHolland, J.H., (1975) Adaptation in Natural and Artificial Systems, , University of Michigan Press, An Arbor, MIHormik, K., Stinchcombe, M., White, H., Multi-layer feedforward networks are universal approximators (1989) Neural Networks, 2 (5), pp. 359-366Moussi, L.N., Gudwin, R.R., Von Zuben, F.J., Madrid, M.K., Neural networks in classifier systems (NNCS): An application to autonomous navigation (2001) Advances in Signal Processing Robotics and Communications, pp. 256-262. , In V.V. Kluev & N.E. Mastorakis (eds.)Electrical and Computer Engineering Series, WSES PressRamsey, C.L., Schultz, A.C., Grefenstette, J.J., Simulation-assisted learning by competition: Effects of noise differences between training model and target environment (1990) Proceedings of the Seventh International Conference on Machine Learning, Austin, TX, pp. 211-215. , Morgan KaufmannRichards, R.A., Zeroth-order shape optimization utilizing a learning classifier system (1995), http://www.stanford.edu/buc/SPHINcsX/book.html, Ph.D. Thesis, Mechanical Eng. Dept., Stanford UniversitySchultz, A.C., Grefenstette, J.J., Using a genetic algorithm to learn behaviors for autonomous vehicles Proceedings of the AIAA Guidance, Navigation and Control Conference, Hilton Head, SC, August 10-12, 1992, , Navy Center for Applied Research in Artificial Intelligence, Navy Research Laboratory, Washington, DCWebots Release 3.0.1, , www.cyberbotics.com, (for evaluation purpose only), from Cyberbotics Lt

Hierarchical Evolution Of Heterogeneous Neural Networks

This paper describes a hierarchical evolutionary technique developed to design and train feedforward neural networks with different activation functions on their hidden-layer neurons (heterogeneous neural networks). At the upper level, a genetic algorithm is used to determine the number of neurons in the hidden layer and the type of the activation function of those neurons. At the second level, neural nets compete against each other across generations so that the nets with the lowest test errors survive. Finally, on the third level, a co-evolutionary approach is used to train each of the created networks by adjusting both the weights of the hidden-layer neurons and the parameters for their activation functions. © 2002 IEEE.217751780Coelho, A.L.V., Weingaertner, D., Von Zuben, F.J., Evolving heterogeneous neural networks for classification problems (2001) Procs. of Genetic and Evolutionary Compufalion Conference (GECCO-2001), pp. 266-273. , Morgan Kaufmann Publishers, JulyWhitehead, B., Choate, T., Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction (1996) IEEE Trans. on Neural Networks, 714, pp. 869-480Moriarty, D.E., Miikkulainen, R., Efficient reinforcement learning through symbiotic evolution (1996) Machine Learning, 22, pp. 11-33. , Kluwcr Academic Publishers, BostonMoriarty, D.E., Miikkulaincn, R., Hierarchical evolution of neural networks (1998) Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 428-433Whitley, D., Genetic algorithms and neural networks (1995) Genetic Algorithms in Engineering and Computer Science, , Periaux, J. and Winter, G. eds John Wiley & Sons LtdIyoda, E., Von Zuben, F., Evolutionary hybrid composition of activation functions in feedforward neural networks (1999) Procs. IJCNN, , article #396Ribeiro, J., Vasconcelos, G., An experimental evaluation of the cascade-correlation network in pattem recognition problems (1997) Proc. of ICONIP, pp. 1133-1136. , Springer-Verlag, New ZelandRibeiro, J., Vasconcelos, G., Constructive neural networks for pattern classification and verification (1999) Proc. of ICONIP, , Springer-VerlagPrechelt, L., (1994) Probeni: A Set of Neural Benchmarking Rules, , TR 21/94, Univcrstat KarlsruheZhao, Q., A coevolutionary algorithm for neural net learning (1997) Procs. of ICNN, 1, pp. 432-437Parekh, R., Yang, J., Honavar, V., (1998) Constructive Neural Network Leaming Algorithms for Multi-Category Real-valued Pattem Classification, pp. 97-06. , TR, Dep. of Computer Science, Iowa State UniversityReed, R., Pruning Algorithms - A Survey (1993) IEEE Trans. on Neural Networks, 45, pp. 740-747Haykin, S.S., (1998) Rleural Networks: A Comprehensive Foundation, , Prentice HallBack, T., Fogel, D.B., Michalewicz, T., (2000) Evolutionary Computation 1: Basic Algorithms and Operators, , Institute of Physics PublishingYao, X., A review of evolutionary artificial neural networks (1993) Int. J. Intell. Syst, 8 (4), pp. 539-567Liu, Y., Yao, X., Evolutionary design of artificial neural networks with different nodes (1996) Procs. of the Third IEEE International Con on Evolutionary Computation (CEC96), pp. 670-675. , Japan MayMichalewicz, Z., Nazhiyath, G., Michalewicz, M., A note on the usefulness of geometrical crossover for numerical optimization problems (1996) Proc. 5th Ann. Conf. on Evolutionary Programming, , MIT Pres

Message From Web 2 Touch 2008 Workshop Chairs

[No abstract available