Maria L. Gini

Partitioning-based clustering for Web document categorization

Abstract Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document structures to define a distance or similarity among these documents, or use probabilistic techniques such as Bayesian classification. Many of these traditional algorithms, however, falter when the dimensionality of the feature space becomes high relative to the size of the document space. In this paper, we introduce two new clustering algorithms that can effectively cluster documents, even in the presence of a very high dimensional feature space. These clustering techniques, which are based on generalizations of graph partitioning, do not require pre-specified ad hoc distance functions, and are capable of automatically discovering document similarities or associations. We conduct several experiments on real Web data using various feature selection heuristics, and compare our clustering schemes to standard distance-based techniques, such as hierarchical agglomeration clustering , and Bayesian classification methods, such as AutoClass .

WebACE: a Web agent for document categorization and exploration

We propose an agent for exploring and categorizing documents on the World Wide Web based on a user pro le. The heart of the agent is an automatic categorization of a set of documents, combined with a process for generating new queries used to search for new related documents and ltering the resulting documents to extract the set of documents most closely related to the starting set. The document categories are not given a-priori. The resulting document set could also be used to update the initial set of documents. We present the overall architecture and describe two novel algorithms which provide signi cant improvement over traditional clustering algorithms and form the basis for the query generation and search component of the agent.

Magma an agent based virtual market for electronic commerce

We propose an architecture for an agent-based virtual market that includes all elements required for simulating a real market. These elements include a communication infrastructure, mechanisms for storage and transfer of goods, banking and monetary transactions, and economic mechanisms for direct or brokered producer-consumer transactions. We report findings that resulted from implementing and conducting experiments with a free-market agent architecture (MAGMA). MAGMA is an extensible architecture that provides all services essential to agent-based commercial activities. These services are available through an open-standard messaging API, which allows using a heterogeneous set of agents, independently of the platform and language.

Document Categorization and Query Generation on the World Wide WebUsing WebACE

We present WebACE, an agent for exploring and categorizing documents onthe World Wide Web based on a user profile. The heart of the agent is anunsupervised categorization of a set of documents, combined with a processfor generating new queries that is used to search for new relateddocuments and for filtering the resulting documents to extract the onesmost closely related to the starting set. The document categories are notgiven a priori. We present the overall architecture and describe twonovel algorithms which provide significant improvement over HierarchicalAgglomeration Clustering and AutoClass algorithms and form the basis forthe query generation and search component of the agent. We report on theresults of our experiments comparing these new algorithms with moretraditional clustering algorithms and we show that our algorithms are fastand sacalable.

A market architecture for multi-agent contracting

We present a generalized market architecture that provides support for a variety of types of transactions, from simple buying and selling of goods and services to complex multi-agent contract negotiations. This architecture is organized around three basic com- ponents: the exchange, the market, and the session. We also present a negotiation protocol for planning by contracting that takes advantage of the services of the market. We show how the existence of an appropriate market infrastructure can add value to a multi-agent contracting protocol by controlling fraud and discouraging counterspeculation.

Enlisting rangers and scouts for reconnaissance and surveillance

Reconnaissance and surveillance are important activities for both military and civilian organizations, for hostage and survivor rescue, drug raids, response to chemical or toxic waste spills etc. We have developed a distributed heterogeneous robotic team that is based mainly on a miniature robotic system. Because some operations require covert action, most of the robots are extremely small. This also allows them to be easily transported and allows for a greater number to be brought into use for a single operation. This makes them expendable without jeopardizing the overall mission. We call these small robots scouts. Their individual components must all be exceedingly small, and their overall design must make maximum use of all available space. They must make efficient use of resources (e.g., batteries). We meet these challenges with an innovative design and creative use of additional support. We team the scouts with larger ranger robots, which can transport the scouts over distances of several kilometers, deploy them rapidly over a large area, coordinate their behavior, and collect and present the resulting data. We present the scouts and rangers, discuss their capabilities along with the associated software, and describe demonstrations conducted to test the innovative aspects of the system. We also discuss related work, analyze our results, and draw conclusions.

A Multi-Agent Negotiation Testbed for Contracting Tasks with Temporal and Precedence Constraints

In multi-agent contracting, customer agents solicit the resources and capabilities of other agents, sometimes executing multistep tasks in which tasks are contracted out to different suppliers. The authors have developed a testbed for studying the decision behaviors of agents in this context. It generates sets of tasks with known statistical attributes, formulates and submits requests for quotations, generates bids with well-defined statistics, and evaluates bids according to several criteria. Each of these processes is supported by an abstract interface and a series of pluggable modules with numerous configuration parameters, and with data collection and analysis tools.

Repeated auctions for robust task execution by a robot team

We present empirical results of an auction-based algorithm for dynamic allocation of tasks to robots. The results have been obtained both in simulation and using real robots. A distinctive feature of our algorithm is its robustness to uncertainties and to robot malfunctions that happen during task execution, when unexpected obstacles, loss of communication, and other delays may prevent a robot from completing its allocated tasks. Therefore tasks not yet achieved are resubmitted for bids every time a task has been completed. This provides an opportunity to improve the allocation of the remaining tasks, enabling the robots to recover from failures and reducing the overall time for task completion.

Autonomous stair-hopping with Scout robots

Search and rescue operations in large disaster sites require quick gathering of relevant information. Both the knowledge of the location of victims and the environmental/structural conditions must be available to safely and efficiently guide rescue personnel. A major hurdle for robots in such scenarios is stairs. A system for autonomous surmounting of stairs is proposed in which a Scout robot jumps from step to step. The robots height is only about a quarter step in size. Control of the Scout is accomplished using visual servoing. An external observer such as another robot is brought into the control loop to provide the Scout with an estimation of its pose with respect to the stairs. This cooperation is necessary as the Scout must refrain from ill-fated motions that may lead it back down to where it started its ascend. Initial experimental results are presented along with a discussion of the issues involved.

Parallel search algorithms for robot motion planning

The authors show that parallel search techniques derived from their sequential counterparts can enable the solution of instances of the robot motion planning problem which are computationally infeasible on sequential machines. A parallel version of a robot motion planning algorithm based on quasibest first search with randomized escape from local minima and random backtracking is presented. Its performance on a problem instance, which was computationally infeasible on a single processor of an nCUBE2 multicomputer, is discussed. The limitations of parallel robot motion planning systems are discussed, and a course for future work is suggested.<<ETX>>