人工智能贝叶斯网络.pptVIP

* * * * * * * * * * * Node Clustering Eliminate all loops by merging nodes to create meganodes that have the cross-product of values of the merged nodes. Number of values for merged node is exponential in the number of nodes merged. Still reasonably tractable for many network topologies requiring relatively little merging to eliminate loops. * Bayes Nets Applications Medical diagnosis Pathfinder system outperforms leading experts in diagnosis of lymph-node disease. Microsoft applications Problem diagnosis: printer problems Recognizing user intents for HCI Text categorization and spam filtering Student modeling for intelligent tutoring systems. * Statistical Revolution Across AI there has been a movement from logic-based approaches to approaches based on probability and statistics. Statistical natural language processing Statistical computer vision Statistical robot navigation Statistical learning Most approaches are feature-based and “propositional” and do not handle complex relational descriptions with multiple entities like those typically requiring predicate logic. Structured (Multi-Relational) Data In many domains, data consists of an unbounded number of entities with an arbitrary number of properties and relations between them. Social networks Biochemical compounds Web sites * Biochemical Data Predicting mutagenicity [Srinivasan et. al, 1995] Web-KB Dataset [Slattery Craven, 1998] Faculty Grad Student Research Project Other Collective Classification Traditional learning methods assume that objects to be classified are independent (the first “i” in the i.i.d. assumption) In structured data, the class of an entity can be influenced by the classes of related entities. Need to assign classes to all objects simultaneously to produce the most probable globally-consistent interpretation. Logical AI Paradigm Represents knowledge and data in a binary symbolic logic such as FOPC. + Rich representation that handles arbitrary sets of objects, with properties, relat