Embodied Language Learning with the Humanoid Robot iCub
Growing theoretical and experimental research on action and language processing and on number learning and space representation clearly demonstrates the role of embodiment in cognition. These studies have important implications for the design of communication and linguistic capabilities in cognitive systems and robots, and have led to the new interdisciplinary approach of Cognitive Developmental Robotics. In the European FP7 project “ITALK” (www.italkproject.org) and the Marie Curie ITN “RobotDoC” (www.robotdoc.org) we follow this integrated view of action and language to develop cognitive capabilities in the humanoid robot iCub. During the talk we will present ongoing results from iCub experiments on embodiment biases in early word acquisition studies, word order cues for lexical development and number and space interaction effects. The talk will also introduce the simulation software of the iCub robot, an open source software tool to perform cognitive modeling experiments in simulation.
Angelo Cangelosi is Professor of Artificial Intelligence and Cognition and the Director of the Centre for Robotics and Neural Systems at Plymouth University (UK). Cangelosi studied psychology and cognitive science at the Universities of Rome La Sapienza and at the University of Genoa, and has been visiting scholar at the University of California San Diego and the University of Southampton. Cangelosi's main research expertise is on language and cognitive modelling in humanoid robots, on language evolution in multi-agent systems, and the application of bio-inspired techniques to robot control (e.g. swarm of UAVs). He is the coordinator of the Marie Curie ITN "RobotDoC: Robotics for Development of Cognition" (2009-2013) and the UK EPSRC project “BABEL: Bio-inspired Architecture for Brain Embodied Language” (2012-2016), and of the FP7 project "ITALK” completed in 2012. Cangelosi has produced more than 200 scientific publications, is Editor-in-Chief of the journal Interaction Studies, and has chaired numerous workshops and conferences including the IEEE ICDL-EpiRob 2011 Conference (Frankfurt, August 2011). In 2012 he was nominated Chair of the International IEEE Technical Committee on Autonomous Mental Development.
Ultimate Hacking: Programmable Parallel Problem Solving in vivo
The decision making processes of a biological cell, e.g. a bacterium, often result in a variety of outputs such as the creation of more cells, chemotaxis, bio-film formation, antibiotic production, etc. It was recently shown that even the simplest of cells not only react to their environment but that they can even predict environmental changes. Synthetic Biology (SB) considers “the cell” to be a machine that can be built –from parts– in a manner similar to, e.g., computer programs, electronic circuits, airplanes, etc. and it has sought to co-opt biological cells abilities for nano-computation and nano-manufacturing purposes. In particular, synthetic biological programs that implement Boolean logic gates such as NOT gates and AND gates and other small-scale in vivo information processing tasks have been demonstrated in the laboratory. This talk presents an approach based on "Executable Biology" (also called "Algorithmic Systems/Synthetic Biology") for the specification, analysis and execution of parallel programs for living entities. The methodology enables the formal specification of programs for individual cells and the scaling up towards parallel multicellular computing systems. During the talk I will demonstrate how the proposed techniques have been used in practice (i.e. in the wet lab) to calculate in vivo Turing Patterns and what the current capabilities and limitations are. Time permitting, I will detail what I believe are key areas of research opportunities in Synthetic Biology for the PPSN community.
Natalio Krasnogor is Professor of Applied Interdisciplinary Computing in the School of Computer at the University of Nottingham where he leads the Interdisciplinary Computing and Complex Systems (ICOS) Research Group (http://icos.cs.nott.ac.uk)
Krasnogor's research activities lie at the interface of Computer Science and the Natural Sciences, e.g. Biology, Physics, Chemistry. In particular, he develops methodologies for transdisciplinary optimisation, modelling of complex systems and very-large datasets processing and he also carries out research on algorithmic living matter.
He was associate editor of the Evolutionary Computation journal and is founding technical editor-in-chief of the Memetic Computing journal.
Krasnogor won several evolutionary computation related awards, e.g., the best overall paper award at the 2007 IEEE Congress on Evolutionary Computation (CEC 2007), best paper awards in GECCO 2008 & 2010, Bronze prize at the 2007 "HUMIES" Awards for Human-Competitive Results produced by Genetic and Evolutionary Computation, Gold prize at the 2010 "HUMIES" edition and the 2010 ACM's Special Interest Group on Evolutionary Computation Impact Award for the most highly cited paper of those published in a GECCO proceeding 10 years earlier.
Krasnogor's Synthetic Biology work attracted international media attention with interviews appearing The Register, Slashdot, NewScientist, Science Magazine, The Discovery Channel, CNN, etc.
Global equilibrium search algorithm for combinatorial optimization problems
Global Equilibrium Search (GES) is a meta-heuristic search method that shares similar ideas with simulated annealing method. GES accumulates a compact set of information about search space of an optimization problem that is used to generate promising initial solutions for local search techniques. This method has been successfully applied to classic discrete optimization problems, such as the unconstrained quadratic programming problem, the maximum satisfiability problem, the max-cut problem, the multidimensional knapsack problem and the job-shop scheduling problem. On all these domains, GES provides state-of-the-art performance compared to the current best known algorithms when used for large scale problems. In this talk, we provide an overview of Global Equilibrium Search, and discuss some successful applications. We explain counter-intuitive empirical observations of super linear speedup in parallel implementations and reveal how parallel acceleration is linked to restart properties of underlying serial algorithms.
This is joint work with Dmytro Korenkevych and Oleg Shylo.
Dr. Panos Pardalos is Distinguished Professor of Industrial and Systems Engineering at the University of Florida. He is also affiliated faculty member of the Computer Science Department, the Hellenic Studies Center, and the Biomedical Engineering Department. He is also the director of the Center for Applied Optimization.
Dr. Pardalos obtained a PhD degree from the University of Minnesota in Computer and Information Sciences. He has held visiting appointments at Princeton University, DIMACS Center, Institute of Mathematics and Applications, FIELDS Institute, AT&T Labs Research, Trier University, Linkoping Institute of Technology, and Universities in Greece.
He has received numerous awards including, University of Florida Research Foundation Professor, UF Doctoral Dissertation Advisor/Mentoring Award, Foreign Member of the Royal Academy of Doctors (Spain), Foreign Member Lithuanian Academy of Sciences, Foreign Member of the Ukrainian Academy of Sciences, Foreign Member of the Petrovskaya Academy of Sciences and Arts (Russia), and Honorary Member of the Mongolian Academy of Sciences.
Dr. Pardalos received the degrees of Honorary Doctor from Lobachevski University (Russia) and the V.M. Glushkov Institute of Cybernetics (Ukraine), he is a fellow of AAAS, a fellow of INFORMS, and in 2001 he was awarded the Greek National Award and Gold Medal for Operations Research.
Dr. Pardalos is a world leading expert in global and combinatorial optimization. He is the editor-in-chief of the Journal of Global Optimization, Journal of Optimization Letters, and Computational Management Science. In addition, he is the managing editor of several book series, and a member of the editorial board of several international journals. He is the author of 8 books and the editor of several books. He has written numerous articles and developed several well known software packages. His research is supported by National Science Foundation and other government organizations. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.
Dr. Pardalos has been an invited lecturer at many universities and research institutes around the world. He has also organized several international conferences.
Biological Evolution as a Form of Learning
Living organisms function according to protein circuits. Darwin's theory of evolution suggests that these circuits have evolved through variation guided by natural selection. However, the question of which circuits can so evolve in realistic population sizes and within realistic numbers of generations has remained essentially unaddressed.
We suggest that computational learning theory offers the framework for investigating this question, of how circuits can come into being adaptively from experience, without a designer. We formulate evolution as a form of learning from examples. The targets of the learning process are the functions of highest fitness. The examples are the experiences. The learning process is constrained so that the feedback from the experiences is Darwinian. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. The dilemma is that if the function class, say for the expression levels of proteins in terms of each other, is too restrictive, then it will not support biology, while if it is too expressive then no evolution algorithm will exist to navigate it. We shall review current work in this area.
Leslie Valiant was educated at King's College, Cambridge; Imperial College, London; and at Warwick University where he received his Ph.D. in computer science in 1974. He is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University, where he has taught since 1982. Before coming to Harvard he had taught at Carnegie Mellon University, Leeds University, and the University of Edinburgh.
His work has ranged over several areas of theoretical computer science, particularly complexity theory, computational learning, and parallel computation. He also has interests in computational neuroscience, evolution and artificial intelligence.
He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, the European Association for Theoretical Computer Science EATCS Award in 2008, and the 2010 A. M. Turing Award. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).