TARK 2015

Abstracts of Invited talks and Note on Speakers

• Robin Clark on "Quine's Topiary: Coordination and Change in an Artificial Society":
  This talk reports the results of a large Agent-Based model of phoneticvariation; each agent in the society has its own unique representation of the signal space, yet the agents are able to coordinate their signaling behavior. We show a number of results: First, agents in a segregated but egalitarian society will blend their signals overtimeif they signal to each other; agents in a segregated, but bigoted, society will maintain stable variation. Second, if the artificial society contain high status leaders---that is, the society is not egalitarian---then the signal space will actually move apart, creating variation where none existed before. We will analyze the source of this variation and show that it is a potential source of language variation and language change. Finally, we will discuss the relationship between private knowledge and social convention.
  
  
  
  
• Simon Huttegger on "The Problem of Analogical Inference in Inductive Logic":
  We consider one problem that was largely left open by Rudolf Carnap in his work on inductive logic, the problem of analogical inference. After discussing some previous attempts to solve this problem, we propose a new solution that is based on the ideas of Bruno de Finetti on probabilistic symmetries. We explain how our new inductive logic can be developed within the Carnapian paradigm of inductive logic - deriving an inductive rule from a set of simple postulates about the observational process - and discuss some of its properties.
  
  Note on speaker:
  Simon Huttegger studied philosophy and mathematics at the University of Salzburg and obtained his Doctorate in 2006. After a two-year postdoc at the Konrad Lorenz Institute for Evolution and Cognition Research, he moved to UC Irvine in 2008, where he has been an Associate Professor since 2010. He is working on game- and decision theory, philosophy of biology, the foundations of probability, and inductive logic.
  
  
  
  
• Sarit Kraus on "Human Agent Decision Making: Combining theory and practice":
  Extensive work has been conducted both in game theory and logic to model strategic interaction.Can we use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand,people do not necessarily adhere to playing in accordance with these strategies, and behavior is affected by a multitude of social and psychological factors. In this talk we will consider the question of whether strategies implied by the theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory.
  
  Note on speaker:
  Sarit Kraus (Ph.D. Computer Science, Hebrew University, 1989) is a Professor of Computer Science at Bar-Ilan University and an Adjunct Professor at the University of Maryland. Her research is focused on intelligent agents and multi-agent systems (including people and robots). She has also contributed to the research on homeland security, adversarial patrolling, social networks and nonmonotonic reasoning. Kraus was awarded the IJCAI Computers and Thought Award, the ACM SIGART Agents Research award, the EMET prize and was twice the winner of the IFAAMAS influential paper award. She is an ACM, AAAI and ECCAI fellow and a recipient of the advanced ERC grant. She also received a special commendation from the city of Los Angeles, together with Prof. Tambe, Prof. Ordonez and their USC students, for the creation of the ARMOR security scheduling system. She has published over 350 papers in leading journals and major conferences. She is the author of the book "Strategic Negotiation in Multiagent Environments" (2001) and a co-author of the books "Heterogeneous Active Agents" (2000) and "Principles of Automated Negotiation" (2014). Kraus is a senior associate editor of the Annals of Mathematics and Artificial Intelligence Journal and an associate editor of the Journal of Autonomous Agents and Multi-Agent Systems. She is a member of the board of directors of the International Foundation for Multi-agent Systems (IFAAMAS).
  
  
  
  
• Marciano Siniscalchi on "Rationality and Beliefs in Dynamic Games: Revealed-Preference Foundations":
  Sequential rationality requires that players maximize their continuation payoff even at information sets to which they assign zero probability. This paper explores the revealed-preference foundations for sequential rationality. It proposes a novel decision criterion, the "sequential preferences" model. A strategy that is optimal for sequential preferences is also sequentially rational. Furthermore, sequential preferences can be elicited on the basis of observable choices. In particular, sequential preferences provide a theoretical rationale for the "strategy method," a commonly used experimental procedure. A variant of the strategy method can also be used to elicit conditional beliefs both on and off the expected path of play, in an incentive-compatible way.
  
  Note on speaker:
  Marciano Siniscalchi is Professor of Economics at Northwestern University. His research focuses on decision theory and game theory. In particular, Siniscalchi is interested in the epistemic analysis of dynamic games. He is Co-editor of the Journal of Economic Theory, as well as Associate Editor of Econometrica, and Foreign Editor of the Review of Economic Studies.
  
  
  
  
• Tutorial on "Recent Methodological Advances in Causal Discovery and Inference":
  This tutorial talk aims to give a broad coverage of emerging approaches to causal inference and causal discovery from i.i.d data and from time series, with both theoretical and practical results. We review concepts and principles involved in reasoning about causal relations, introduce some recent developments in causal discovery and inference, and discuss possibilities of interdisciplinary study of causality.
  We start with the constraint-based approach to causal discovery, which relies on the conditional independence relationships in the data, and discuss its wide applicability as well as its drawbacks. We then talk about the identifiability of the causal structure implied by appropriately defined functional causal models: in the two-variable case, under what conditions (and why) is the causal direction between the two variables identifiable? We show that the independence between the noise and causes, together with appropriate structural constraints on the functional form, makes it possible. Next, we report some recent advances in causal discovery from time series. Assuming that the causal relations are linear with non-Gaussian noise, we focus on two problems which are traditionally difficult to solve, namely, causal discovery from subsampled data and that in the presence of confounding time series. Finally, we briefly discuss some related issues, including the implications of causal knowledge in machine learning and potential problems with causal discovery based on functional causal models.
  
  Note on speakers:
  Peter Spirtes is a Professor of Philosophy at Carnegie Mellon University. He is one of the co-authors of Causation, Prediction, and Search, and of Discovering Causal Structure. His research centers on Analyzing the assumptions used in causal inference, and on the design, application, proofs of correctness, and analysis of the computational complexity of causal inference algorithms.
  
  Kun Zhang is currently a senior research scientist at Max Planck Institute for Intelligent Systems, Germany, and a lead scientist at information sciences institute, University of Southern California. In August, 2015, he will join the philosophy department at Carnegie Mellon University as an assistant professor. His main research interests include causal discovery, machine learning, and large-scale data analysis. He has served as a co-organizer of a series of workshops to foster interdisciplinary research in causality.