- Technical Program
- Workshops & Tutorials
- At a glance
- Doctoral Consortium
- Opening & Reception
- Best Papers from Sister Conferences Track
- IJCAI Video Track
- Trading Agent Competion (TAC)
- IJCAI-11 Awards
- Funding Opportunities for International Research Collaborations
- General Game Playing Competition
- Ramon Llull Session
- Industry Day
- Closing Event
- List of Accepted Papers
- Poster Boards
TUTORIALS ARE FREE FOR IJCAI ATTENDEES
July 16-18, 2011
Tutors: Kristian Kersting, Sriraam Natarajan and David Poole
Recent years have seen an explosion of successes in combining probability and (subsets of) first-order logic respectively. These have led to a surge in interest in lifted probabilistic inference algorithms that exploit redundancies to speed-up inference, ultimately avoiding explicit state enumeration by manipulating first-order state representations directly. The goal of this first-of-its-kind one day tutorial on lifted inference is to get the authors of the original lifted inference algorithms to present their work in their own words to the benefit of students and researchers aspiring to work in probabilistic logical models. This tutorial will be of immense use to motivated students and AI researchers in general who wish to pursue research in probabilistic relational models and statistical relational AI. The presenters include Eyal Amir, Pedro Domingos, Lise Getoor, Kristian Kersting, Brian Milch, Sriraam Natarajan, David Poole, Rodrigo de Salvo Braz, and Prithviraj Sen.
Tutors: Edith Elkind, Georgios Chalkiadakis and Michael Wooldridge
Cooperative game theory has an important role in the theoretical foundations of multi-agent systems, as a powerful set of models and solution concepts through which cooperation in both natural and artificial social systems can be modelled and understood. The aim of this tutorial is to provide a self-contained, comprehensive, and authoritative overview of research on computational aspects of cooperative game theory. The target audience will be AI researchers who want to gain an insight into the concepts and techniques of cooperative game theory, issues surrounding the use of these techniques in computational settings, key approaches developed to date to tackle these challenges, applications of cooperative game theory, and future research issues.
Tutors: Alessandro Farinelli, Jesús Cerquides, Sarvapali D. Ramchurn,Pedro Meseguer, Alex Rogers and Juan A. Rodriguez-Aguilar
The number of novel applications of multi-agent systems has followed an exponential trend over the last few years, ranging from online auction design, through in multi-sensor networks, to scheduling of tasks in multi-actor systems. Multi-agent systems designed for all these applications generally require some form of optimisation in order to achieve their goal. This tutorial will present state of the art solution techniques for optimisation problems in different areas of multi-agent systems, particularly focusing on market based resource allocation, coalition formation and cooperative decentralised decision making. Moreover, open questionsand promising future research directions will be highlighted. The potential targets are PhD students and researchers who have a basic background on AI techniques for optimisation and are interested in the field of Multi-Agent Systems, or conversely that have been working on multi-agent systems and want to learn more about optimisation techniques that could be applied in this field.
Tutors: Martin Gebser and Torsten Schaub
Answer Set Programming (ASP) is a declarative problem solving approach, combining a rich yet simple modeling language with high-performance solving capacities. ASP is particularly suited for modeling problems in the area of Knowledge Representation and Reasoning involving incomplete, inconsistent, and changing information. This tutorial will present a practical introduction to Answer Set Programming (ASP), aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while practically illustrating the overall solving process via existing applications. ASP approach is not only highly suitable for the practitioner solving an AI problem at hand but also for disseminating many basic AI techniques through teaching their (executable) formalization in ASP.
Tutors: Fabian M. Suchanek, Martin Theobald, Gerhard Weikum, Hady W. Lauw and Ralf Schenkel
The advent of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web sources has enabled the automatic construction of large knowledge bases. Recent endeavors of this kind include academic research projects such as DBpedia, EntityCube, KnowItAll, ReadTheWeb, and YAGO-NAGA, as well as industrial ones such as Freebase and Trueknowledge. These projects provide automatically-constructed, large and rich knowledge bases of facts about named entities, their semantic classes, and their mutual relations. This 1-day tutorial will discuss a) the content, organization, and potential of these Web-induced knowledge bases, b) state-of-the-art methods for constructing them from semistructured and textual Web sources, c) recent approaches to maintaining and extending them, which includes introducing a temporal dimension of knowledge, d) use cases of knowledge bases, including semantic search, reasoning for question answering, and entity linking and disambiguation. It is likely to interest a broad audience of AI researchers because it bridges the areas of data and text mining, knowledge extraction, knowledge-based search, and uncertain data management. It will also point out open problems and research opportunities on this spectrum of issues.
T6: Satisfiability Modulo Theories
Tutor: Roberto Sebastiani
Satisfiability Modulo Theories (SMT) is the problem of deciding the satisfiability of a (typically quantifier-free) first-order formula with respect to some decidable first-order theory (e.g., those of linear arithmetic, Arrays, and Bit-Vectors) or of combinations of different theories. SMT is being recognized as increasingly important due to its applications in many domains in different communities. This tutorial aims at providing an overview of the main problems, techniques, functionalities and applications of SMT. The tutorial is directed to a rather general AI audience, in particular to people interested in various domains of Automated Reasoning and Knowledge Representation, like SAT, decision procedures, reasoning in modal and description logics, planning, temporal reasoning, and also to those people interested in applications of automated reasoning techniques for formal verification. The tutorial assumes only a basic knowledge of computer science, logic and AI topics. A background on SAT is of help, but it is not strictly necessary.
Tutor: Eric I. Hsu
An active and promising line of recent research has been to combine techniques from probablistic reasoning over graphical models with techniques for constraint satisfaction, in order to solve problems from either area. This tutorial will first present a formal synthesis of probabilistic and constraint reasoning in terms of the factor graph representation, and then will proceed to review the emerging line of combined research in light of this framework, while identifying opportunities for future research along the way. The tutorial is directed to a broad AI audience. The basic concepts will be easily approachable for novices, while experts may be interested in the more advanced topics within their own areas of specialization, as well as the entire set of foundational techniques in the area other than their main specialization.
T8: Constraint Processing from the Graphical Model Perspective
Tutor: Rina Dechter
Constraint networks can be viewed within the general frameork of graphical models which includes also belief networks, Markov random fields and influence diagrams. Graphical models are knowledge representation schemes that capture independencies in the knowledge base and support efficient graph-based algorithms for a variety of tasks. The tutorial will present the algorithmic principles behind the progress that has been made in the past decades in constraint processing for answering a variety of queries such as: determining consistency, finding one or all solutions, finding optimal solutions and counting solutions. We will emphasize connection with the general graphical models such as Probabilistic networks, focusing on optimization queries, likelihood and counting queries. Complexity analysis and empirical demonstration of all algorithms will be presented on variety of benchmarks including radio-frequency problems, linkage analysis, combinatorial auctions, and coding networks.
T9: Graphical Languages for Preference Representation and Applications
Tutors: Sylvain Bouveret and Jerome Lang
The specification of a decision making problem includes the agents preferences on the available alternatives. The choice of a model of preferences (e.g., utility functions or binary relations) does not say how preferences should be represented (or specified). This tutorial we will give a survey of graphical languages for compact preference representation. We will first survey languages for ordinal preferences, with a special focus on CP-nets; then we will survey languages for numerical preferences. The last part of the tutorial will be devoted to applications of these languages to various AI fields, namely constrained optimization, planning, recommender systems, configuration, voting, and resource allocation. This tutorial is directed to AI researchers who work on related areas and want/need to know more about preference representation: researchers in knowledge representation, constraints, autonomous agents, multiagent systems (especially game-theoretic agents and computational social choice), planning, user modelling. There is very little prerequisite knowledge; the tutorial will be accessible to almost all AI researchers.
Tutor: Jussi Rintanen
During the last ten years a number of algorithmic breakthroughs have lifted the efficiency of domain-independent planning to the level required in many real-world applications. The focus of the tutorial is in the algorithmic basis of different forms of state-space traversal which are needed in efficiently solving a wide range of AI planning problems stretching from the simplest forms of deterministic/classical planning to conditional planning and more general forms of multi-agent planning and game-theoretic planning. The tutorial is intended for the general AI community audience and only assumes basic knowledge in AI and the propositional logic.
Tutor: Hector Geffner
Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case. In this tutorial, we will look at the variety of models used in AI planning, and the techniques that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is comprehensive but not shallow. The target audience of this tutorial is students and researchers interested in autonomous behavior and cognitive science.
T12: Recommender Systems
Tutors: Dietmar Jannach, Markus Zanker and Gerhard Friedrich
Recommender Systems help users navigating through large product assortments, making decisions in e-commerce scenarios and overcome information overload. Probably the most prominent example is the book recommendation service of the e-tailer Amazon.com. This system takes the behavior, opinions and tastes of a large community of users into account and thus constitutes a social or collaborative recommendation approach. In contrast, content-based approaches rely on product features and textual item descriptions. Knowledge-based algorithms, finally, generate item recommendations based on explicit knowledge models from the domain. The tutorial will offer an introduction to the various basic strategies and methods for building Recommender Systems and will discuss the different known approaches and how the effectiveness of such systems can be determined. It is likely to interest a broad audience of AI researchers because it bridges several areas such as Information Retrieval, Text Classification, Machine Learning and Decision Support Systems.
Tutors: Sebastian Rudolph and Baris Sertkaya
The standardization of OWL by W3C as the standard ontology language for the Semantic Web has led to a widespread use of OWL ontologies in more and more application domains accompanied by an increasing tool support for OWL. This tutorial will present methods and tools that support the ontology engineer in detecting missing information in an ontology and enhancing it in such a way that it becomes complete in a specific, formally defined sense. These methods are based on a mathematically well-founded interactive knowledge acquisition method called attribute exploration, which has been developed in Formal Concept Analysis and proved successful in many practical scenarios by today. The potential target audience of the tutorial contains researchers working in the fields of ontologies, semantic web and their theoretical foundations, as well as ontology engineers from different application domains where ontologies are used, like life sciences and bio-medical computer science.
Tutors: Alexander Artikis, Georgios Paliouras and Francois Portet
The recognition of events in the multitude of data streams that are being recorded, ranging from business process data to computer and sensor network data, is becoming ever more important. This tutorial aims to show how artificial intelligence methods, based on logic, provide a sound and effective approach to event recognition. Recognition systems with a logic-based representation of event structures have been attracting considerable attention because, among others, they exhibit a formal, declarative semantics, they have proven to be efficient and scalable, and they are supported by machine learning tools, automating the construction and refinement of event structures. In this tutorial, we will review representative approaches of logic-based event recognition, and discuss open research issues of this field. More precisely, we will present a purely temporal reasoning system that has proven to be very efficient, a system for temporal and atemporal representation and reasoning, and a system that explicitly deals with uncertainty. The intended audience of the tutorial consists of academics, students and practitioners investigating the open issues of event recognition, and/or willing to apply event recognition techniques for extracting knowledge from structured and unstructured datasets.
Tutors: Alexandre Termier, Anne Laurent and Shirish Tatikonda
Data mining consists in extracting valid, novel, potentially useful and ultimately understandable patterns in data. It relies on applying complex and time consuming algorithms to the data in order to extract patterns of interest. Nowadays, the volume of data to handle is huge, and the patterns to extract are more and more complex, making data mining solutions hardly scalable to real world data. Data mining researchers, and especially specialists of frequent pattern mining have started investigating new algorithms dedicated to multi-core processors. This tutorial will provide a panorama of the interest of parallelism for data mining algorithms, with a focus on frequent pattern mining. After an introduction by a specialist of the domain of parallelism, the tutorial will provide a general presentation of parallel data mining algorithms, with a review of existing works and of today's challenges. The tutorial will end with a specialized talk, delving deeper on parallel pattern mining.
T16: Web Mining
Tutors: Ricardo Baeza-Yates and Aris Gionis
The Web continues to grow and evolve very fast, changing our daily lives. This activity represents the collaborative work of the millions of institutions and people that contribute content to the Web as well as the one billion people that use it. In this ocean of hyperlinked data there is explicit and implicit information and knowledge. Web Mining is the task of analyzing this data and extracting information and knowledge for many different purposes. The data comes in three main flavors: content (text, images, etc.), structure (hyperlinks) and usage (navigation, queries, etc.), implying different techniques such as text, graph or log mining. Each case reflects the wisdom of some group of people that can be used to make the Web better, for example, user generated tags in Web 2.0 sites. In this tutorial we will walk through the mining process and will show several applications, ranging from Web site design to search engines. The main goal is to introduce AI researchers to the myriad of challenges in Web mining, where other AI techniques, in addition to machine learning, might be applicable.