Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. The challenge in this model is that each of these agents must learn about its environment— the existence, knowledge, diversity, reliability, and trustworthiness of other agents — by analyzing the queries received from and results exchanged with these other agents. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results. A prototype of 6S is available for public use and is intended to assist in the evaluation of different AI techniques employed by the networked agents.

In the past, many knowledge representation systems failed because they were too monolithic and didn’t scale well, whereas other systems failed to have an impact because they were small and isolated. Along with this trade-off in size, there is also a constant tension between the cost involved in building a larger community that can interoperate through common terms and the cost of the lack of interoperability. The semantic web offers a good compromise between these approaches as it achieves wide-scale communication and interoperability using finite effort and cost. The semantic web is a set of standards for knowledge representation and exchange that is aimed at providing interoperability across applications and organizations. We believe that the gathering success of this technology is not derived from the particular choice of syntax or of logic. Its main contribution is in recognizing and supporting the fractal patterns of scalable web systems. These systems will be composed of many overlapping communities of all sizes, ranging from one individual to the entire population that have internal (but not global) consistency. The information in these systems, including documents and messages, will contain some terms that are understood and accepted globally, some that are understood within certain communities, and some that are understood locally within the system. The amount of interoperability between interacting agents (software or human) will depend on how many communities they have in common and how many ontologies (groups of consistent and related terms) they share. In this article we discuss why fractal patterns are an appropriate model for web systems and how semantic web technologies can be used to design scalable and interoperable systems.

This introduction to AI Magazine's Special Issueon Networks and AI summarizes the seven articles in thespecial issue by characterizing the nature of thenetworks that are the focus of each of the papers.A short tutorial on graph theory and network structuresis included for those less familiar with the topic.

The Association for the Advancement of Artificial Intelligence (AAAI) was pleased to present the AAAI 2008 Spring Symposium Series, held Wednesday through Friday, March 26–28, 2008 at Stanford University, California. The titles of the eight symposia were as follows: (1) AI Meets Business Rules and Process Management, (2) Architectures for Intelligent Theory-Based Agents, (3) Creative Intelligent Systems, (4) Emotion, Personality, and Social Behavior, (5) Semantic Scientific Knowledge Integration, (6) Social Information Processing, (7) Symbiotic Relationships between Semantic Web and Knowledge Engineering, (8) Using AI to Motivate Greater Participation in Computer Science The goal of the AI Meets Business Rules and Process Management AAAI symposium was to investigate the various approaches and standards to represent business rules, business process management and the semantic web with respect to expressiveness and reasoning capabilities. The focus of the Architectures for Intelligent Theory-Based Agents AAAI symposium was the definition of architectures for intelligent theory-based agents, comprising languages, knowledge representation methodologies, reasoning algorithms, and control loops. The Creative Intelligent Systems Symposium included five major discussion sessions and a general poster session (in which all contributing papers were presented). The purpose of this symposium was to explore the synergies between creative cognition and intelligent systems. The goal of the Emotion, Personality, and Social Behavior symposium was to examine fundamental issues in affect and personality in both biological and artificial agents, focusing on the roles of these factors in mediating social behavior. The Semantic Scientific Knowledge Symposium was interested in bringing together the semantic technologies community with the scientific information technology community in an effort to build the general semantic science information community. The Social Information Processing's goal was to investigate computational and analytic approaches that will enable users to harness the efforts of large numbers of other users to solve a variety of information processing problems, from discovering high-quality content to managing common resources. The goal of the Symbiotic Relationships between the Semantic Web and Software Engineering symposium was to explore how the lessons learned by the knowledge-engineering community over the past three decades could be applied to the bold research agenda of current workers in semantic web technologies. The purpose of the Using AI to Motivate Greater Participation in Computer Science symposium was to identify ways that topics in AI may be used to motivate greater student participation in computer science by highlighting fun, engaging, and intellectually challenging developments in AI-related curriculum at a number of educational levels. Technical reports of the symposia were published by AAAI Press.

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

RuleML-2008 will be held October 30-31, 2008 in Orlando, Florida, USA. ICAART 2009 will be held January 19-21, 2009, in Porto, Portugal. This page includes all the AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. IUI 2009 will be 11-15, 2010, in Atlanta, Georgia USA. held February 8-11, 2009, on Sanibel ICINCO 2009 will be Twenty-First International Joint be held May 19-21, 2009, at the Sundial held July 1-5, 2009, in Milan, Italy. IJCAI-09 will be held July 11-Island, FL. 17, 2009, in Pasadena, California.

Citizens, both young and feeds, and semistructured metadata old, are also discovering how social media in the form of extensible markup language technology can improve their lives and (XML) and resource description give them more voice in the world. We they provide more useful, trustworthy, begin by describing an overarching task of and reliable. Pursuing this task uncovers It differs, however, in ways a number of problems that must be addressed, that affect how it should be modeled, analyzed, three of which we describe in and exploited. The first is recognizing spam model for the general web is as a directed graph of web pages with undifferentiated in the form of spam blogs (splogs) and links between pages. The second is developing has a much richer network structure more effective techniques to recognize in that there are more types of nodes the social structure of blog communities. For example, the abstract model for the underlying blog people who contribute to blogs and au-network structure and how it evolves. Figure 2 shows a hypothetical blog graph and its corresponding flow of information in the influence graph. Studies on influence in social networks and collaboration graphs have typically focused on the task of identifying key individuals who play an important role in propagating information. This is similar to finding authoritative pages on the web.

The Seventeenth International Conference on Automated Planning and Scheduling (ICAPS-07) was held in Providence, Rhode Island in September 2007. It covered the latest theoretical and practical advances in planning and scheduling. The conference was co-located with the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP-07). The program consisted of tutorials, workshops, system demonstrations, a doctoral consortium, and three days of technical presentations mostly in parallel sessions. ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling. This report describes the conference in more detail.

A large class of systems of biological and technological relevance can be described as analog networks, that is, collections of dynamical devices interconnected by links of varying strength. Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Analog networks are typically complex systems which include nonlinear feedback loops and possess temporal dynamics at different time scales. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. In this paper we will discuss the general relevance of the analog network concept and describe an evolutionary approach to the automatic synthesis and the reverse engineering of analog networks. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. AGE permits the evolution of human-competitive solutions to real-world analog network design and identification problems. This is illustrated by some examples of application to the design of electronic circuits, control systems, learning neural architectures, and the reverse engineering of biological networks.

The normalized information distance is a universal distance measure for objects of all kinds. It is based on Kolmogorov complexity and thus uncomputable, but there are ways to utilize it. First, compression algorithms can be used to approximate the Kolmogorov complexity if the objects have a string representation. Second, for names and abstract concepts, page count statistics from the World Wide Web can be used. These practical realizations of the normalized information distance can then be applied to machine learning tasks, expecially clustering, to perform feature-free and parameter-free data mining. This chapter discusses the theoretical foundations of the normalized information distance and both practical realizations. It presents numerous examples of successful real-world applications based on these distance measures, ranging from bioinformatics to music clustering to machine translation.