If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The heuristics used for planning and search often take the form of pattern databases generated from abstracted versions of the given state space. Pattern databases are typically stored as lookup tables with one entry for each state in the abstract space, which limits the size of the abstract state space and therefore the quality of the heuristic that can be used with a given amount of memory. In the AIPS-2002 conference Stefan Edelkamp introduced an alternative representation, called symbolic pattern databases, which, for the Blocks World, required two orders of magnitude less memory than a lookup table to store a pattern database. This paper presents experimental evidence that Edelkamp's result is not restricted to a single domain. Symbolic pattern databases, in the form of Algebraic Decision Diagrams, are one or more orders of magnitude smaller than lookup tables on a wide variety of problem domains and abstractions.
Consider a 0-1 observation matrix M, where rows correspond to entities and columns correspond to signals; a value of 1 (or 0) in cell (i,j) of M indicates that signal j has been observed (or not observed) in entity i. Given such a matrix we study the problem of inferring the underlying directed links between entities (rows) and finding which entries in the matrix are initiators. We formally define this problem and propose an MCMC framework for estimating the links and the initiators given the matrix of observations M. We also show how this framework can be extended to incorporate a temporal aspect; instead of considering a single observation matrix M we consider a sequence of observation matrices M1,..., Mt over time. We show the connection between our problem and several problems studied in the field of social-network analysis. We apply our method to paleontological and ecological data and show that our algorithms work well in practice and give reasonable results.
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.
Balduccini, Marcello (Eastman Kodak Company) | Baral, Chitta (Arizona State University) | Brodaric, Boyan (Geological Survey of Canada) | Colton, Simon (Imperial College, London) | Fox, Peter (National Center for Atmospheric Research) | Gutelius, David (SRI International) | Hinkelmann, Knut (University of Applied Sciences Northwestern Switzerland) | Horswill, Ian (Northwestern University) | Huberman, Bernardo (HP Labs) | Hudlicka, Eva (Psychometrix Associates) | Lerman, Kristina (USC Information Sciences Institute) | Lisetti, Christine (Florida International University) | McGuinness, Deborah L. (Rensselaer Polytechnic Institute) | Maher, Mary Lou (National Science Foundation) | Musen, Mark A. (Stanford University) | Sahami, Mehran (Stanford University) | Sleeman, Derek (University of Aberdeen) | Thönssen, Barbara (University of Applied Sciences Northwestern Switzerland) | Velasquez, Juan D. (MIT CSAIL) | Ventura, Dan (Brigham Young University)
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 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 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.
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). ICAPS-07 also hosted the second edition of the International Competition on Knowledge Engineering for Planning and Scheduling.
Mattiussi, Claudio (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Marbach, Daniel (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Dürr, Peter (Swiss Federal Institute of Technology in Lausanne (EPFL)) | Floreano, Dario (Swiss Federal Institute of Technology in Lausanne (EPFL))
Some examples of analog networks are genetic regulatory networks, metabolic networks, neural networks, analog electronic circuits, and control systems. Both the synthesis and reverse engineering of analog networks are recognized as knowledge-intensive activities, for which few systematic techniques exist. The proposed approach is called analog genetic encoding (AGE) and realizes an implicit genetic encoding of analog networks. 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.
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. Its main contribution is in recognizing and supporting the fractal patterns of scalable web systems. 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.
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. 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.