AI

Abstract: Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once youve found it.

Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:

• Collaborative filtering techniques that enable online retailers to recommend products or media
• Methods of clustering to detect groups of similar items in a large dataset
• Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm
• Optimization algorithms that search millions of possible solutions to a problem and choose the best one
• Bayesian filtering, used in spam filters for classifying documents based on word types and other features
• Using decision trees not only to make predictions, but to model the way decisions are made
• Predicting numerical values rather than classifications to build price models
• Support vector machines to match people in online dating sites
• Non-negative matrix factorization to find the independent features in a dataset
• Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game

Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you.

Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details.
-- Dan Russell, Google

Tobys book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths.
-- Tim Wolters, CTO, Collective Intellect

 algorithm;  agents;  AI;  clustering;  toolkit;  web20;  bayesian;  collective computing;  geneticalgorithms;  hacks;  information-systems;  

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Abstract: The use of the related terms “agent-based”, “multi-agent”, “software agent” and “intelligent agent” have witnessed significant growth in the Geographic Information Science (GIScience) literature in the past decade. These terms usually refer to both artificial life agents that simulate human and animal behavior and software agents that support human-computer interactions. In this article we first comprehensively review both types of agents. Then we argue that both these categories of agents borrow from Artificial Intelligence (AI) research, requiring them to share the characteristics of and be similar to AI agents. We also argue that geospatial agents form a distinct category of AI agents because they are explicit about geography and geographic data models. Our overall goal is to first capture the diversity of, and then define and categorize GIScience agent research into geospatial agents, thereby capturing the diversity of agent-oriented architectures and applications that have been developed in the recent past to present a holistic review of geospatial agents.

 GIS;  agents;  AI;  artificial intelligence;  geographic;  artificial life;  

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 agents;  AI;  intelligence analysis;  

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 agents;  AI;  knowledge-discovery;  collaborative;  

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 agents;  AI;  

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 Knowledge Representation;  AI;  

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 AI;  cartographic generalization;  generalization;  

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 spatial data models;  AI;  image understanding;  

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 auto cart;  AI;  

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 AI;  titme-space data models;  

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 AI;  spatial reasoning;  

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 AI;  Australia;  

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 AI;  time;  

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 AI;  generalization;  

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 DEMS;  KBGIS;  AI;  

Abstract: The goal of this research is to develop a formal system based on Michalskis variable-valued logic calculus that characterizes different patterns of plausible inference humans use in reasoning about the world. The core theory presented consists of 3 parts: 1. a formal representation of plausible inference patterns; such as deductions, inductions and analogies 2. a set of parameters, such as conditional likelihood, typicality and similarity 3. A system relating the different plausible inference patterns and the different certainty parameters.

 Knowledge Representation;  AI;  reasoning;  

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 auto cart;  object-oriented;  AI;  

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 AI;  Australia;  

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 gen gis;  AI;  

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 spatial representation;  DEM's;  AI;  G459;  

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 GIS;  AI;  

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 GIS;  AI;  

Abstract: One of the most significant issues that NASA must address and resolve is the problem of managing the enormous amounts of scientific and engineering data that will be generated by the next generation of remote sensing systems, such as the Hubble Space Telescope and the Earth Observation System. The amount of data these sensors are expected to produce will be orders of magnitude greater than NASA has ever experienced. Consequently new solutions must be developed for managing, accessing and automatically inputting the data into a database in some expressive fashion that will provide a meaningful understanding and effective utilization of this data in a multidisciplinary environment. Currently, scientific data provided by satellites and other sources (i.e., in situ measurements) are processed, cataloged, and archived accoring to narrow mission or project-specific requirements with little regard to the semantics of the overall research. Scientists therefore lack knowledge of or access to potentially valuable data outside their own field. What is needed is an innovative approach that will allow collected data to be automatically cataloged, characterized and managed in a domain-specific context and made available interactively and in near-real-time to the user community.

 GIS;  AI;  large databases;  

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 DEM's;  AI;  

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 user interfaces;  Visualization;  heterogeneous databases;  AI;  

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 spatial representation;  DEM's;  AI;  G459;  

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 KBGIS;  AI;  

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 KBGIS;  AI;  spatial languages;  

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 hierarchical data structures;  object-oriented;  AI;  

Abstract: Rips-off my dual-model idea!! The authors report a novel interface to a spatial analysis system which allows the underlying geographical domain to be represented using a high-level, feature-or object-oriented model. The system incorporates an intelligent, data-driven reporting technique which analyses the domain characteristics in order to highlight significant trends. A second novel feature is the use of data held in our extended data model for the purpose of query optimization. The interface is based on object-oriented techniques, which have been enhanced to capture contextual data and knowledge, including general patterns of data behaviour, general geographical knowledge and database domain-specific characteristics. A geographical information system can offer more flexibility when it can handle searches in both a spatial and object-centred way. Our current work is concerned with increasing the functionality and efficiency of the object-centred approach, and hence increasing the effectiveness of geographical information systems as an aid to analysis and decision-making, especially where very large volumes of data are involved.

 KBGIS;  AI;