S

IJCAI Conference 2016 Conference Paper

An Empirical Game-Theoretic Analysis of Price Discovery in Prediction Markets

• Elaine Wah
• S
• eacute; bastien Lahaie
• David M. Pennock

In this paper, we employ simulation-based methods to study the role of a market maker in improving price discovery in a prediction market. In our model, traders receive a lagged signal of a ground truth, which is based on real price data from prediction markets on NBA games in the 2014-2015 season. We employ empirical game-theoretic analysis to identify equilibria under different settings of market maker liquidity and spread. We study two settings: one in which traders only enter the market once, and one in which traders have the option to reenter to trade later. We evaluate welfare and the profits accrued by traders, and we characterize the conditions under which the market maker promotes price discovery in both settings.

IJCAI Conference 2016 Conference Paper

Clustering Financial Time Series: How Long Is Enough?

• Gautier Marti
• S
• eacute; bastien Andler
• Frank Nielsen
• Philippe Donnat

Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.

IJCAI Conference 2016 Conference Paper

Is Promoting Beliefs Useful to Make Them Accepted in Networks of Agents?

• Nicolas Schwind
• Katsumi Inoue
• Gauvain Bourgne
• S
• eacute; bastien Konieczny
• Pierre Marquis

We consider the problem of belief propagation in a network of communicating agents, modeled in the recently introduced Belief Revision Game (BRG) framework. In this setting, each agent expresses her belief through a propositional formula and revises her own belief at each step by considering the beliefs of her acquaintances, using belief change tools. In this paper, we investigate the extent to which BRGs satisfy some monotonicity properties, i. e. , whether promoting some desired piece of belief to a given set of agents is actually always useful for making it accepted by all of them. We formally capture such a concept of promotion by a new family of belief change operators. We show that some basic monotonicity properties are not satisfied by BRGs in general, even when the agent's merging-based revision policies are fully rational (in the AGM sense). We also identify some classes where they hold.

IJCAI Conference 2016 Conference Paper

On Consensus Extraction

• Eacute; ric Gr
• eacute; goire
• S
• eacute; bastien Konieczny
• Jean Marie Lagniez

Computing a consensus is a key task in various AI areas, ranging from belief fusion, social choice, negotiation, etc. In this work, we define consensus operators as functions that deliver parts of the set-theoretical union of the information sources (inpropositional logic) to be reconciled, such that no source is logically contradicted. We also investigate different notions of maximality related to these consensuses. From a computational point of view, we propose a generic problem transformation that leads to a method that proves experimentally efficient very often, even for large conflicting sources to be reconciled.

IJCAI Conference 2016 Conference Paper

Predicting Confusion in Information Visualization from Eye Tracking and Interaction Data

• S
• eacute; bastien Lall
• eacute;
• Cristina Conati
• Giuseppe Carenini

Confusion has been found to hinder user experience with visualizations. If confusion could be predicted and resolved in real time, user experience and satisfaction would greatly improve. In this paper, we focus on predicting occurrences of confusion during the interaction with a visualization using eye tracking and mouse data. The data was collected during a user study with ValueChart, an interactive visualization to support preferential choices. We report very promising results based on Random Forest classifiers.

IJCAI Conference 2015 Conference Paper

Extension Enforcement in Abstract Argumentation as an Optimization Problem

• Sylvie Coste-Marquis
• S
• eacute; bastien Konieczny
• Jean-Guy Mailly
• Pierre Marquis

Change in abstract argumentation frameworks (AFs) is a very active topic. Especially, the problem of enforcing a set E of arguments, i. e. , ensuring that E is an extension (or a subset of an extension) of a given AF F, has received a particular attention in the recent years. In this paper, we define a new family of enforcement operators, for which enforcement can be achieved by adding new arguments (and attacks) to F (as in previous approaches to enforcement), but also by questioning some attacks (and non-attacks) of F. This family includes previous enforcement operators, but also new ones for which the success of the enforcement operation is guaranteed. We show how the enforcement problem for the operators of the family can be modeled as a pseudo-Boolean optimization problem. Intensive experiments show that the method is practical and that it scales up well.

IJCAI Conference 2015 Conference Paper

On the Aggregation of Argumentation Frameworks

• J
• eacute; r
• ocirc; me Delobelle
• S
• eacute; bastien Konieczny
• Srdjan Vesic

We study the problem of aggregation of Dung’s abstract argumentation frameworks. Some operators for this aggregation have been proposed, as well as some rationality properties for this process. In this work we study the existing operators and new ones that we propose in light of the proposed properties, highlighting the fact that existing operators do not satisfy a lot of these properties. The conclusions are that on one hand none of the existing operators seem fully satisfactory, but on the other hand some of the properties proposed so far seem also too demanding.

IJCAI Conference 2015 Conference Paper

The Adjusted Winner Procedure: Characterizations and Equilibria

• Haris Aziz
• Simina Br
• acirc; nzei
• Aris Filos-Ratsikas
• S
• oslash; ren Kristoffer Stiil Frederiksen

The Adjusted Winner procedure is an important mechanism proposed by Brams and Taylor for fairly allocating goods between two agents. It has been used in practice for divorce settlements and analyzing political disputes. Assuming truthful declaration of the valuations, it computes an allocation that is envy-free, equitable and Pareto optimal. We show that Adjusted Winner admits several elegant characterizations, which further shed light on the outcomes reached with strategic agents. We find that the procedure may not admit pure Nash equilibria in either the discrete or continuous variants, but is guaranteed to have -Nash equilibria for each > 0. Moreover, under informed tiebreaking, exact pure Nash equilibria always exist, are Pareto optimal, and their social welfare is at least 3/4 of the optimal.

IJCAI Conference 2011 Conference Paper

An Interaction-Oriented Model for Multi-Scale Simulation

• S
• eacute; bastien Picault
• Philippe Mathieu

The design of multiagent simulations devoted to complex systems, addresses the issue of modeling behaviors that are involved at different space, time, behavior scales, each one being relevant so as to represent a feature of the phenomenon. We propose here a generic formalism intended to represent multiple environments, endowed with their own spatiotemporal scales and with behavioral rules for the agents they contain. An environment can be nested inside any agent, which itself is situated in one or more environments. This leads to a lattice decomposition of the global system, which appears to be necessary for an accurate design of multi-scale systems. This uniform representation of entities and behaviors at each abstraction level relies upon an interaction-oriented approach for the design of agent simulations, which clearly separates agents from interactions, from the modeling to the code. We also explain the implementation of our formalism within an existing interaction-based platform.

IJCAI Conference 2011 Conference Paper

Belief Base Rationalization for Propositional Merging

• S
• eacute; bastien Konieczny
• Pierre Marquis
• Nicolas Schwind

Existing belief merging operators take advantage of all the models from the bases, including those contradicting the integrity constraints. In this paper, we show that this is not suited to every merging scenario. We study the case when the bases are "rationalized" with respect to the integrity constraints during the merging process. We define in formal terms several independence conditions for merging operators and show how they interact with the standard IC postulates for belief merging. Especially, we give an independence-based axiomatic characterization of a distance-based operator.

IJCAI Conference 2011 Conference Paper

LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data

• Axel-Cyrille Ngonga Ngomo
• S
• ouml; ren Auer

The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5\% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases. Yet, this task requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES, a novel time-efficient approach for link discovery in metric spaces. Our approach utilizes the mathematical characteristics of metric spaces during the mapping process to filter out a large number of those instance pairs that do not suffice the mapping conditions. We present the mathematical foundation and the core algorithms employed in LIMES. We evaluate our algorithms with synthetic data to elucidate their behavior on small and large data sets with different configurations and compare the runtime of LIMES with another state-of-the-art link discovery tool.

AAMAS Conference 2010 Conference Paper

Everything can be Agent!

• Yoann Kubera
• Philippe Mathieu
• S
• eacute; bastien Picault

Most Multi-Agent System designers use several notions - like "agent", "artifact", "object", etc. – to classify the entities involved in simulations. These notions require different methodologies, data structures and algorithms. In thispaper, we show that the representation of entities can befavorably unified. As a consequence, the design and implementation process are made easier, since the designer hasno longer to assign a fixed type to each entity during modelconstruction. The implementation handles entities throughan unified data structure and algorithm, and is thereforelightweight and more maintainable. Such an unification isperformed without efficiency loss in a concrete simulationmethodology called Ioda. According to common sense, wepropose to call such an unified entity simply "agent"!

IJCAI Conference 2007 Conference Paper

• Christophe Lecoutre
• Lakhdar Sais
• S
• eacute; bastien Tabary
• Vincent Vidal

In this paper, nogood recording is investigated within the randomization and restart framework. Our goal is to avoid the same situations to occur from one run to the next one. More precisely, nogoods are recorded when the current cutoff value is reached, i. e. before restarting the search algorithm. Such a set of nogoods is extracted from the last branch of the current search tree. Interestingly, the number of nogoods recorded before each new run is bounded by the length of the last branch of the search tree. As a consequence, the total number of recorded nogoods is polynomial in the number of restarts. Experiments over a wide range of CSP instances demonstrate the effectiveness of our approach.

IJCAI Conference 2007 Conference Paper

• S
• oslash; ren Tjagvad Madsen
• Gerhard Widmer

This paper presents first steps towards a simple, robust computational model of automatic melody identification. Based on results from music psychology that indicate a relationship between melodic complexity and a listener's attention, we postulate a relationship between musical complexity and the probability of a musical line to be perceived as the melody. We introduce a simple measure of melodic complexity, present an algorithm for predicting the most likely melody note at any point in a piece, and show experimentally that this simple approach works surprisingly well in rather complex music.

AAMAS Conference 2005 Conference Paper

An Online POMDP Algorithm for Complex Multiagent Environments

• S
• eacute; bastien Paquet
• Ludovic Tobin
• Brahim Chaib-draa

AAMAS Conference 2005 Conference Paper

Emerging Collective Behavior in a Simple Artificial Financial Market

• Roberto da Silva
• Ana Bazzan
• Alexandre Baraviera
• S
• iacute; lvio Dahmen

IJCAI Conference 2005 Conference Paper

Solving Checkers

• J. Schaeffer
• Y. Björnsson
• N. Burch
• A. Kishimoto
• M. Müller
• R. Lake
• P. Lu
• S

AI has had notable success in building highperformance game-playing programs to compete against the best human players. However, the availability of fast and plentiful machines with large memories and disks creates the possibility of a game. This has been done before for simple or relatively small games. In this paper, we present new ideas and algorithms for solving the game of checkers. Checkers is a popular game of skill with a search space of possible positions. This paper reports on our first result. One of the most challenging checkers openings has been solved – the White Doctor opening is a draw. Solving roughly 50 more openings will result in the game-theoretic value of checkers being determined.

IJCAI Conference 1975 Conference Paper

"QUESTION-ANSWER** - A Multipurpose Information System

• Y. Buchstab
• S