Anthony Hunter Title: Introduction to Computational Models of Argument (Tutorial) - Sun 21/10 14:00 (slides[pdf]) Abstract: Computational Models of Argument are being developed with the aim of reflecting how human argumentation uses conflicting information to construct and analyse arguments. Argumentation involves identifying arguments and counterarguments relevant to an issue (e.g. What are the pros and cons for the safety of mobile phones for children?). Argumentation may also involve weighing, comparing, or evaluating arguments (e.g. What sense can we make of the arguments concerning mobile phones for children?) and it may involve drawing conclusions (e.g. A parent answering the question "Are mobile phones safe for my children?"). In addition, argumentation may involve convincing an audience (e.g. A politician making the case that mobile phones should be banned for children because the risk of radiation damage is too great). In this tutorial, we will consider both graph-based and logic-based formalizations of argumentation, introducing some of the basic concepts, and reviewing a range of proposals and results. Title: Computational Models of Dialogical Argumentation (Talk) - Tue 23/10 8:30 (slides[pdf])     Abstract: Computational models of dialogical argumentation aim to reflect how humans use and resolve conflicting information, opinions, beliefs, etc. by exchanging and analysing arguments. This may be as part of a wider process such as decision making, sense making, or negotiation. In these formal models, a dialogue between two or more agents is normally made up of a set of communicative acts, called moves, a set of rules that state which moves are legal to make at any point in a dialogue (the protocol), and a set of rules that define the effect of making a move. In addition, each agent may have a private strategy that specifies which of the allowed moves it should do in the current state of the dialogue given its goals. In this talk, we will consider some of the main approaches to modelling dialogical argumentation, and identify some of the many research questions that have been identified.

Scott Sanner Title: Recent Advances in Continuous Planning (Tutorial) - Sun 21/10 16:30 Abstract: This tutorial will outline the importance of continuous variables and actions in deterministic and stochastic planning and how one can cope with the complexity of planning for such problems in practice. The first part of the tutorial will focus on modeling real-world phenomena both abstractly (deterministic transition models, MDPs, POMDPs) and in terms of formal planning languages (PDDL, PPDDL, and RDDL). The second part of the tutorial will focus on solution methods, ranging from search in deterministic settings, through to approximation methods such as discretization and sampling.From this point, the tutorial will shift to an in-depth focus on very recent work that allows the exact solution of an expressive range of continuous planning problems via a technique referred to as Symbolic Dynamic Programming. The tutorial will conclude by briefly connecting continuous planning to related fields such as Control Theory and Scheduling. Title: Data Structures for Efficient Inference and Optimization in Expressive Continuous Domains (Talk) - Tue 23/10 16:30 (slides[pdf])     Abstract: To date, our ability to perform exact closed-form inference or optimization with continuous variables is largely limited to special well-behaved cases.  This talk argues that with an appropriate representation and data structure, we can vastly expand the class of models for which we can perform exact, closed-form inference. This enables novel solutions to many problems of interest in AI, machine learning, operations research and beyond, some of which have been unsolved for 50+ years. This talk is in two parts.  In the first part, I introduce an extension of the algebraic decision diagram (ADD) to continuous variables -- termed the extended ADD (XADD) -- to represent arbitrary piecewise functions (nb, arbitrary pieces, not just hyper-rectangular) over discrete and continuous variables and show how to define and efficiently compute elementary arithmetic operations, integrals, and maximization for various restrictions of these functions.  In the second part, I briefly cover a wide range of novel applications where the XADD may be applied: (a) exact inference in expressive discrete and continuous variable graphical models, (b) factored, parameterized linear and quadratic optimization (a generalization of LP and QP solving), (c) exact solutions to piecewise convex functions that enable a number of novel applications in machine learning, and (d) exact solutions to continuous state, action, and observation sequential decision-making problems -- which includes closed-form solutions to previously unsolved problems in operations research. This is joint work with Zahra Zamani & Ehsan Abbasnejad (Australian National University), Karina Valdivia Delgado & Leliane Nunes de Barros (University of Sao Paulo), and Simon Fang (M.I.T.).

Alan Kirman Title: Individual and Collective Rationality in Complex Systems (Talk) - Mon 22/10 16:30 Abstract: This talk will analyse the emergence of collective behaviour which has a certain rationality from the point of view of the aggregate but which does not correspond to that of the individual. In economics it has long been recognised that, as a result of “externalities” behaviour that is rational from the point of view of the individual may lead to collective disasters as in the case of the “Tragedy of the Commons”. However, even more interesting are the cases where individuals who interact locally and have only limited information can together produce outcomes which have regularities which could not have been predicted from the individual motives. These may be beneficial but may also, as a result of contagion, lead the system to sudden collapse. Modelling this sort of phenomena usually involves starting with a formal analysis of a very simple case and then simulating a more complex and realistic system to see whether the results for the simple case still hold in the more general case. I will illustrate this with a number of examples, from some observations about the analogies with social insects, and ant and bees in particular, to recent work on the extension of the Schelling model of segregation. In the latter we include preferences for neighbourhood income as well as racial composition and introduce a housing market. We analyse segregation in terms of both race and income and the relation between house prices and these factors. I will also discuss a model of the market for mortgage backed securities and show how the rational reactions of a few individuals to a small change in the probability of default on the underlying assets can lead to a collapse in the price of the assets. In each case the self organisation of a complex adaptive system produces aggregate results which could not have been predicted from the analysis of a “representative agent”.

Leandro de Castro Title: Natural Computing: The Grand Challenges and Two Case Studies (Talk) - Wed 24/10 16:30 Abstract: Although the origins of Natural Computing (NC) can be traced back to the early days of Computer Science in the 1940s, it only became a formal discipline around the year 2000. Since then, there has been a flood of research papers on the use of Nature to inspire the design of novel algorithms. Now, it is felt that the field faces some Grand Challenges so as to progress and mature. A new perspective of the field has emerged, broadening its scope, and three Grand Challenges have been proposed: to transform Natural Computing into a transdisciplinary science; to harness information processing in natural systems; and to formally and consistently engineer Natural Computing approaches. This talk introduces and discusses the Grand Challenges in Natural Computing Research and then, for showing the pragmatism and usefulness of NC, follows with the presentation of two real-world applications of Natural Computing, one for Social Media and another in e-Commerce.