19572588
Web Site Personalization Using User Profile Information
In this paper we discuss a technique for web site personalization. Connectivity analysis has been shown to be useful in identifying high quality web pages within a topic or domain specific graph of hyper linked documents. We have implemented a system that creates a view of a subset of a web site most relevant to a given user. This sort of personalization is useful for filtering a useful subset of a site so that the user gets a low volume of quality information. The essence of our approach is to augment a previous connectivity analysis with content analysis. We present an agent which assists the user when he browses and distills a personalized sub graph of the website based on his user profile.
4723885
Learning while solving problems in best first search
We investigate the role of learning in search-based systems for solving optimization problems. We use a learning model, where the values of a set of features can be used to induce a clustering of the problem state space. The feasible set of h* values corresponding to each cluster is called h*set. If we relax the optimality guarantee, and tolerate a risk factor, the distribution of h*set can be used to expedite search and produce results within a given risk of suboptimality. The off-line learning method consists of solving a batch of problems by using A* to learn the distribution of the h*set in the learning phase. This distribution can be used to solve the rest of the problems effectively. We show how the knowledge acquisition phase can be integrated with the problem solving phase. We present a continuous online learning scheme that uses an "anytime" algorithm to learn continuously while solving problems.
40802326
Constructing Efficient Belief Network Structures With Expert Provided Information
Presents a technique to construct efficient belief network structures for application areas where large amounts of data are available and information on the ordering of the variables can be obtained from domain experts. We identify classes of networks that are efficient for propagating beliefs. We formulate the problem as one of determining the belief network representation from a given class that best represents the data. We use the I-Divergence measure which is known to have certain desirable properties for evaluating different approximations. We present some theoretical findings that characterize the nature of solutions that are obtained. These theoretical results lead to an efficient solution procedure for finding the best network representation. We also discuss other information that may be reasonably obtained from experts, and show how such information leads to improving the efficiency of the technique to find the best network structure.
476243
Reconciling Attribute Values from Multiple Data Sources
Because of the heterogeneous nature of multiple data sources, data integration is often one of the most challenging tasks of today’s information systems. While the existing literature has focused on problems such as schema integration and entity identification, our current study attempts to answer a basic question: When an attribute value for a real-world entity is recorded differently in two databases, how should the “best” value be chosen from the set of possible values? We first show how probabilities for attribute values can be derived, and then propose a framework for deciding the cost-minimizing value based on the total cost of type I, type II, and misrepresentation errors.
29720896
Assessing data quality for information products
Data quality is a critical issue in the design and implementation of databases. Often, the information provided to decision-makers is derived from heterogeneous databases. To determine the quality characteristics of information provided to decision-makers (derived data), it is necessary to determine how quality characteristics associated with diverse data sources affect the quality of the derived data. We discuss a methodology to determine two data quality characteristics, accuracy and incompleteness, that are of critical importance to decision-makers. We examine how the quality metrics of source data affect metrics for data derived using relational algebra operations.
16820690
Introducing Local Optimization for Effective Initialization and Crossover of Genetic Decision Trees
We introduce a new genetic operator, Reduction, that recties decision trees not correct syntactically and at the same time removes the redundant sections within, while preserving its accuracy during operation. A novel approach to crossover is presented that uses the reduction operator to systematically extract building blocks spread out over the entire second parent to create a subtree that is valid and particularly useful in the context it replaces the subtree in the rst parent. The crossover introduced also removes unexplored code from the ospring and hence prevents redundancy and bloating. Overall, reduction can be viewed as a local optimization step that directs the population, generated initially or over generations through crossovers, to potentially good regions in the search space so that reproduction is performed in a highly correlated landscape with a global structure. Lexical convergence is also ensured implying identical individuals always produce the same ospring.
1183276
Effective Use of Memory in Iterative Deepening Search
Abstract The Iterative Deepening A∗ (IDA∗) (R.E. Korf, Artificial Intelligence 27 (1985)) algorithm often reexpands too many nodes while solving certain combinatorial problems. Algorithm IDA∗_CR (U.K. Sarkar, Artificial Intelligence 50 (1991)) attempted to remedy this drawback. These algorithms require very little memory although much more is available in practice. This paper introduces an algorithm IDA∗_CRM which shows how the available memory can be advantageously utilized in IDAast;_CR in order to reduce the number of expanded nodes. IDA∗_CRAM, an approximation scheme derived from IDA∗_CRM, is also presented. Computational results are shown for the Flow-Shop Scheduling Problem.