eacute;

IJCAI Conference 2016 Conference Paper

Change Detection Using Directional Statistics

• Tsuyoshi Id
• eacute;
• Dzung T. Phan
• Jayant Kalagnanam

This paper addresses the task of change detection from noisy multivariate time-series data. One major feature of our approach is to leverage directional statistics as the noise-robust signature of time-series data. To capture major patterns, we introduce a regularized maximum likelihood equation for the von Mises-Fisher distribution, which simultaneously learns directional statistics and sample weights to filter out unwanted samples contaminated by the noise. We show that the optimization problem is reduced to the trust region subproblem in a certain limit, where global optimality is guaranteed. To evaluate the amount of changes, we introduce a novel distance measure on the Stiefel manifold. The method is validated with real-world data from an ore mining system.

IJCAI Conference 2016 Conference Paper

Incentivizing Intelligent Customer Behavior in Smart-Grids: A Risk-Sharing Tariff & Optimal Strategies

• Georgios Methenitis
• Michael Kaisers
• Han La Poutr
• eacute;

Current electricity tariffs for retail rarely provide incentives for intelligent demand response of flexible customers. Such customers could otherwise contribute to balancing supply and demand in future smart grids. This paper proposes an innovative risk-sharing tariff to incentivize intelligent customer behavior. A two-step parameterized payment scheme is proposed, consisting of a prepayment based on the expected consumption, and a supplementary payment for any observed deviation from the anticipated consumption. Within a game-theoretical analysis, we capture the strategic conflict of interest between a retailer and a customer in a two-player game, and we present optimal, i. e. , best response, strategies for both players in this game. We show analytically that the proposed tariff provides customers of varying flexibility with variable incentives to assume and alleviate a fraction of the balancing risk, contributing in this way to the uncertainty reduction in the envisioned smart-grid.

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 2016 Conference Paper

Query-Driven Repairing of Inconsistent DL-Lite Knowledge Bases

• Meghyn Bienvenu
• Camille Bourgaux
• Fran
• ccedil; ois Goasdou
• eacute;

We consider the problem of query-driven repairing of inconsistent DL-Lite knowledge bases: query answers are computed under inconsistency-tolerant semantics, and the user provides feedback about which answers are erroneous or missing. The aim is to find a set of ABox modifications (deletions and additions), called a repair plan, that addresses as many of the defects as possible. After formalizing this problem and introducing different notions of optimality, we investigate the computational complexity of reasoning about optimal repair plans and propose interactive algorithms for computing such plans. For deletion-only repair plans, we also present a prototype implementation of the core components of the algorithm.

IJCAI Conference 2016 Conference Paper

Towards a White Box Approach to Automated Algorithm Design

• Steven Adriaensen
• Ann Now
• eacute;

To date, algorithms for real-world problems are most commonly designed following a manual, ad-hoc, trial and error approach, making algorithm design a tedious, time-consuming and costly process. Recently, Programming by Optimization (PbO) has been proposed as an alternative design paradigm in which algorithmic choices are left open by design and algorithm configuration methods (e. g. ParamILS) are used to automatically generate the best algorithm for a specific use-case. We argue that, while powerful, contemporary configurators limit themselves by abstracting information that can otherwise be exploited to speed up the optimization process as well as improve the quality of the resulting design. In this work, we propose an alternative white box approach, reformulating the algorithm design problem as a Markov Decision Process, capturing the intrinsic relationships between design decisions and their respective contribution to overall algorithm performance. Subsequently, we discuss and illustrate the benefits of this formulation experimentally.

IJCAI Conference 2015 Conference Paper

Multilateral Negotiation in Boolean Games with Incomplete Information Using Generalized Possibilistic Logic

• Sofie De Clercq
• Steven Schockaert
• Ann Now
• eacute;
• Martine de Cock

Boolean games are a game-theoretic framework in which propositional logic is used to describe agents’ goals. In this paper we investigate how agents in Boolean games can reach an efficient and fair outcome through a simple negotiation protocol. We are particularly interested in settings where agents only have incomplete knowledge about the preferences of others. After explaining how generalized possibilistic logic can be used to compactly encode such knowledge, we analyze how a lack of knowledge affects the agreement outcome. In particular, we show how knowledgeable agents can obtain a more desirable outcome than others.

IJCAI Conference 2015 Conference Paper

On the Resiliency of Unit Propagation to Max-Resolution

• Andr
• eacute; Abram
• eacute;
• Djamal Habet

At each node of the search tree, Branch and Bound solvers for Max-SAT compute the lower bound (LB) by estimating the number of disjoint inconsistent subsets (IS) of the formula. IS are detected thanks to unit propagation (UP) then transformed by max-resolution to ensure that they are counted only once. However, it has been observed experimentally that the max-resolution transformations impact the capability of UP to detect further IS. Consequently, few transformations are learned and the LB computation is redundant. In this paper, we study the effect of the transformations on the UP mechanism. We introduce the notion of UPresiliency of a transformation, which quantifies its impact on UP. It provides, from a theoretical point of view, an explanation to the empirical efficiency of the learning scheme developed in the last ten years. The experimental results we present give evidences of UP-resiliency relevance and insights on the behavior of the learning mechanism.

IJCAI Conference 2015 Conference Paper

Reinforcement Learning from Demonstration through Shaping

• Tim Brys
• Anna Harutyunyan
• Halit Bener Suay
• Sonia Chernova
• Matthew E. Taylor
• Ann Now
• eacute;

Reinforcement learning describes how a learning agent can achieve optimal behaviour based on interactions with its environment and reward feedback. A limiting factor in reinforcement learning as employed in artificial intelligence is the need for an often prohibitively large number of environment samples before the agent reaches a desirable level of performance. Learning from demonstration is an approach that provides the agent with demonstrations by a supposed expert, from which it should derive suitable behaviour. Yet, one of the challenges of learning from demonstration is that no guarantees can be provided for the quality of the demonstrations, and thus the learned behavior. In this paper, we investigate the intersection of these two approaches, leveraging the theoretical guarantees provided by reinforcement learning, and using expert demonstrations to speed up this learning by biasing exploration through a process called reward shaping. This approach allows us to leverage human input without making an erroneous assumption regarding demonstration optimality. We show experimentally that this approach requires significantly fewer demonstrations, is more robust against suboptimality of demonstrations, and achieves much faster learning than the recently developed HAT algorithm.

AAMAS Conference 2012 Conference Paper

An RL approach to Common-Interest Continuous Action Games

• Abdel Rodr
• iacute; guez
• Peter Vrancx
• Ricardo Grau
• Ann Now
• eacute;

IJCAI Conference 2011 Conference Paper

A Theory of Meta-Diagnosis: Reasoning about Diagnostic Systems

• Nuno Belard
• Yannick Pencol
• eacute;
• Michel Combacau

In Model-Based Diagnosis, a diagnostic algorithm is typically used to compute diagnoses using a model of a real-world system and some observations. Contrary to classical hypothesis, in real-world applications it is sometimes the case that either the model, the observations or the diagnostic algorithm are abnormal with respect to some required properties; with possibly huge economical consequences. Determining which abnormalities exist constitutes a meta-diagnostic problem. We contribute, first, with a general theory of meta-diagnosis with clear semantics to handle this problem. Second, we propose a series of typically required properties and relate them between themselves. Finally, using our meta-diagnostic framework and the studied properties and relations, we model and solve some common meta-diagnostic problems.

IJCAI Conference 2011 Conference Paper

Coordinating Logistics Operations with Privacy Guarantees

• Thomas L
• eacute; aut
• eacute;
• Boi Faltings

Several logistics service providers serve a certain number of customers, geographically spread over an area of operations. They would like to coordinate their operations so as to minimize overall cost. At the same time, they would like to keep information about their costs, constraints and preferences private, thus precluding conventional negotiation. We show how AI techniques, in particular Distributed Constraint Optimization (DCOP), can be integrated with cryptographic techniques to allow such coordination without revealing agents' private information. The problem of assigning customers to companies is formulated as a DCOP, for which we propose two novel, privacy-preserving algorithms. We compare their performances and privacy properties on a set of Vehicle Routing Problem benchmarks.

AAMAS Conference 2011 Conference Paper

Culture-related Differences in Aspects of Behavior for Virtual Characters Across Germany and Japan

• Birgit Endrass
• Elisabeth Andr
• eacute;
• Afia Akhter Lipi
• Matthias Rehm
• Yukiko Nakano

Integrating culture as a parameter into the behavioral models of virtual characters to simulate cultural differences is becoming more and more popular. But do these differences affect the user's perception? In the work described in this paper, we integrated aspects of non-verbal behavior as well as communication management behavior into the behavioral models of virtual characters for the two cultures of Germany and Japan in order to find out which of these aspects affect human observers of the target cultures. We give a literature review pointing out the expected differences in these two cultures and describe the analysis of a multi-modal corpus including video recordings of German and Japanese interlocutors. After integrating our findings into a demonstrator featuring a German and a Japanese scenario, we presented the virtual scenarios to human observers of the two target cultures in an evaluation study.

AAMAS Conference 2011 Conference Paper

Decentralized Coordination Of Plug-in Hybrid Vehicles For Imbalance Reduction In A Smart Grid

• Stijn Vandael
• Klaas De Craemer
• Nelis Bouck
• eacute;
• Tom Holvoet
• Geert Deconinck

Intelligent electricity grids, or `Smart Grids', are being introduced at a rapid pace. Smart grids allow the management of new distributed power generators such as solar panels and wind turbines, and innovative power consumers such as plug-in hybrid vehicles. One challenge in Smart Grids is to fulfill consumer demands while avoiding infrastructure overloads. Another challenge is to reduce imbalance costs: after ahead scheduling of production and consumption (the so-called `load schedule'), unpredictable changes in production and consumption yield a cost for repairing this balance. To cope with these risks and costs, we propose a decentralized, multi-agent system solution for coordinated charging of PHEVs in a Smart Grid. Essentially, the MAS utilizes an "intention graph" for expressing the flexibility of a fleet of PHEVs. Based on this flexibility, charging of PHEVs can be rescheduled in real-time to reduce imbalances. We discuss and evaluate two scheduling strategies for reducing imbalance costs: reactive scheduling and proactive scheduling. Simulations show that reactive scheduling is able to reduce imbalance costs by 14%, while proactive scheduling yields the highest imbalance cost reduction of 44%.

AAMAS Conference 2011 Conference Paper

Distributed Cooperation in Wireless Sensor Networks

• Mihail Mihaylov
• Yann-A
• euml; l Le Borgne
• Karl Tuyls
• Ann Now
• eacute;

We present a game-theoretic self-organizing approach for scheduling the radio activity of wireless sensor nodes. Our approach makes each node play a win-stay lose-shift (WSLS) strategy to choose when to schedule radio transmission, reception and sleeping periods. The proposed strategy relies only on local interactions with neighboring nodes, and is thus fully decentralized. This behavior results in shorter communication schedules, allowing to not only reduce energy consumption by reducing the wake-up cycles of sensor nodes, but also to decrease the data retrieval latency. We implement this WSLS approach in the OMNeT++ sensor network simulator where nodes are organized in three topologies -line, grid and random. We compare the performance of our approach to two state-of-the-art scheduling protocols, namely S-MAC and D-MAC, and show that the WSLS strategy brings significant gains in terms of energy savings, while at the same time reduces communication delays. In addition, we show that our approach performs particularly well in large, random topologies.

AAMAS Conference 2011 Conference Paper

Influence of Head Orientation in Perception of Personality Traits in Virtual Agents

• Diana Arellano
• Nikolaus Bee
• Kathrin Janowski
• Elisabeth Andr
• eacute;
• Javier Varona
• Francisco J. Perales

The aim of this research is to explore the influence of static visual cues on the perception of a character's personality traits: extraversion, agreeableness and emotional stability. To measure how users perceived personality, we conducted a web-based study with 133 subjects who rated 54 images of a virtual character with varying head orientations and gaze.

AAMAS Conference 2011 Conference Paper

Integrating power and reserve trade in electricity networks

• Nicolas H
• ouml; ning
• Han Noot
• Han La Poutr
• eacute;

In power markets, the trade of reserve energy will become more important as more intermittent generation is traded. In this work, we propose a novel bidding mechanism for the integration of power and reserve markets. It adds expressivity to reserve bids and facilitates planning.

IJCAI Conference 2011 Conference Paper

Interval-Based Possibilistic Logic

• Salem Benferhat
• Julien Hu
• eacute;
• Sylvain Lagrue
• Julien Rossit

Possibilistic logic is a well-known framework for dealing with uncertainty and reasoning under inconsistent knowledge bases. Standard possibilistic logic expressions are propositional logic formulas associated with positive real degrees belonging to [0, 1]. However, in practice it may be difficult for an expert to provide exact degrees associated with formulas of a knowledge base. This paper proposes a flexible representation of uncertain information where the weights associated with formulas are in the form of intervals. We first study a framework for reasoning with interval-based possibilistic knowledge bases by extending main concepts of possibilistic logic such as the ones of necessity and possibility measures. We then provide a characterization of an interval-based possibilistic logic base by means of a concept of compatible standard possibilistic logic bases. We show that interval-based possibilistic logic extends possibilistic logic in the case where all intervals are singletons. Lastly, we provide computational complexity results of deriving plausible conclusions from interval-based possibilistic bases and we show that the flexibility in representing uncertain information is handled without extra computational costs.

AAMAS Conference 2011 Conference Paper

Solving Delayed Coordination Problems in MAS

• Yann-Micha
• euml; l De Hauwere
• Peter Vrancx
• Ann Now
• eacute;

Recent research has demonstrated that considering local interactions among agents in specific parts of the state space, is a successful way of simplifying the multi-agent learning process. By taking into account other agents only when a conflict is possible, an agent can significantly reduce the state-action space in which it learns. Current approaches, however, consider only the immediate rewards for detecting conflicts. This restriction is not suitable for realistic systems, where rewards can be delayed and often conflicts between agents become apparent only several time-steps after an action has been taken. In this paper, we contribute a reinforcement learning algorithm that learns where a strategic interaction among agents is needed, several time-steps before the conflict is reflected by the (immediate) reward signal.

AAMAS Conference 2008 Conference Paper

A Multi-Agent Platform for Auction-Based Allocation of Loads in Transportation Logistics

• Han Noot
• Valentin Robu
• Han La Poutr
• eacute;
• Willem-Jan van Schijndel

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i. e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. Therefore, the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings.

AAMAS Conference 2008 Conference Paper

Agent-based Patient Admission Scheduling in Hospitals

• Anke K. Hutzschenreuter
• Peter A. N. Bosman
• Ilona Blonk-Altena
• Jan van Aarle
• Han La Poutr
• eacute;

S heduling de isions in hospitals are often taken in a de entralized way. This means that di erent spe ialized hospital units de ide autonomously on patient admissions or operating room s hedules. In this paper we present an agent-based model for the sele tion of an optimal mix for patient admissions. Admitting the right mix of patients is important in order to optimize the resour e usage and patient throughput. Our model is based on an extensive ase analysis, involving data analysis and interviews, ondu ted in a ase study at a large hospital in the Netherlands. We fo us on the oordination of di erent surgi al patient types with probabilisti treatment pro esses involving multiple hospital units. We also onsider the unplanned arrival of other patients requiring (partly) the same hospital resour es. Simulation experiments show the appli ability of our agent-based de ision support tool. The simulation tool allows for the assessment of resour e network usage as a fun tion of di erent poli ies for de ision making. Furthermore, the tool in orporates a rst optimization module for the resour e allo ation of postoperative are beds.

AAMAS Conference 2008 Conference Paper

An Interactive Platform for Auction-Based Allocation of Loads in Transportation Logistics

• Valentin Robu
• Han Noot
• Han La Poutr
• eacute;
• Willem-Jan van Schijndel

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i. e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings. The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies.

AAMAS Conference 2008 Conference Paper

Switching Dynamics of Multi-Agent Learning

• Peter Vrancx
• Karl Tuyls
• Ronald Westra
• Ann Now
• eacute;

This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcement learning agents and replicator dynamics in stateless multi-agent games. More precisely, in this work we use a combination of replicator dynamics and switching dynamics to model multi-agent learning automata in multi-state games. This is the first time that the dynamics of problems with more than one state is considered with replicator equations. Previously, it was unclear how the replicator dynamics of stateless games had to be extended to account for multiple states. We use our model to visualize the basin of attraction of the learning agents and the boundaries of switching dynamics at which an agent possibly arrives in a new dynamical system. Our model allows to analyze and predict the behavior of the different learning agents in a wide variety of multi-state problems. In our experiments we illustrate this powerful method in two games with two agents and two states.

IJCAI Conference 2007 Conference Paper

• Anika Schumann
• Yannick Pencol
• eacute;

Diagnosability of systems is an essential property that determines how accurate any diagnostic reasoning can be on a system given any sequence of observations. Generally, in the literature of dynamic event-driven systems, diagnosability analysis is performed by algorithms that consider a system as a whole and their response is either a positive answer or a counter example. In this paper, we present an original framework for diagnosability checking. The diagnosability problem is solved in a distributed way in order to take into account the distributed nature of realistic problems. As opposed to all other approaches, our algorithm also provides an exhaustive and synthetic view of the reasons why the system is not diagnosable. Finally, the presented algorithm is scalable in practice: it provides an approximate and useful solution if the computational resources are not sufficient.

IJCAI Conference 2007 Conference Paper

• Hao-Chuan Wang
• Rohit Kumar
• Carolyn Penstein Ros
• eacute;
• Tsai-Yen Li
• Chun-Yen Chang

We evaluate a new hybrid language processing approach designed for interactive applications that maintain an interaction with users over multiple turns. Specifically, we describe a method for using a simple topic hierarchy in combination with a standard information retrieval measure of semantic similarity to reason about the selection of appropriate feedback in response to extended language inputs in the context of an interactive tutorial system designed to support creative problem solving. Our evaluation demonstrates the value of using a machine learning approach that takes feedback from experts into account for optimizing the hierarchy based feedback selection strategy.

AAMAS Conference 2003 Conference Paper

Bidding with Decommitment in a Multi-Agent Transportation Model

• P. J. `t Hoen
• D. D. B. van Bragt
• Han La Poutr
• eacute;