Adaptive Negotiation for Agent Based Distributed Manufacturing Scheduling
Manufacturing scheduling problem is typically NP-hard. While traditional heuristic search based approaches have been considered not suitable for dynamic environments because of their inherent centralized nature, agent based approaches are promising for their decentralized, autonomous, coordinated, and rational natures. However, many challenging issues still need to be addressed when applying agent based approaches to complex distributed manufacturing scheduling environments. One of them, namely adaptive negotiation , is to integrate intelligence and rationality into negotiation mechanisms and make the system more adaptive in dynamic environments. This is very important to the manufacturing scheduling problem because the agent based scheduling process is essentially a coordination process among agents. Adaptive negotiation is a way to achieve the coordination in a dynamic scheduling environment. By adaptive negotiation we mean that more intelligence and rationality are integrated into the negotiation mechanism, thus make it adaptive to the changes of the dynamic scheduling environment. To achieve this, issues at three levels have to be addressed: system architecture, agent architecture, and heuristics. At the system architecture level, the system must have the architecture with corresponding characteristics to support adaptive negotiation among agents. At the agent architecture level, an agent must have rational abilities (decision making mechanisms) embedded in the architecture to transfer the knowledge (in our case, negotiation heuristics) and environment conditions into specific negotiation behaviors. The third level is the heuristics level. By this we mean the knowledge that needs to be integrated into the agent negotiation mechanism and can be used by agents in terms of rational decision making.

title: Combining mediation and bidding mechanisms for agent-based manufacturing scheduling (poster)
abstract: 2.  Future manufacturing systems need to be dynamically reconfigurable to produce customized products in small batches, even down to lot sizes of one unit, with fast turnaround in a cost-efficient manner. Such systems must be implemented with dynamic scheduling mechanisms for rapidly responding to emergent production tasks and orders, and other unforeseen situations such as production tardiness and machine failures.

title: An Agent-Based E-engineering Services Framework for Engineering Design and Optimization

title: Agent Based Dynamic Information Gathering And Organization For Distributed Product Development
abstract: The concept of Web-based Visualized Product Structure Tree (VPST) has been proposed to realize a platform-independent, visualized and integrated product information sharing. This paper extends this concept by developing an agent based dynamic information gathering and organization framework to support updating the VPST dynamically. The objective is to enable different users to perform real time collaboration over the Web. The proposed framework is composed of three modules: Information Gathering, Information Organization Server and Information Sharing Server. Each of them is composed of a number of agents. The paper presents a detailed design and implementation of the proposed framework and its three modules.

title: An agent-based Web service workflow model for inter-enterprise collaboration
abstract: The service-orientated computing paradigm is transforming traditional workflow management from a close and centralized control system into a worldwide dynamic business process. A complete workflow serving inter-enterprise collaboration should include both internal processes and ad hoc external processes. This paper presents an agent-based workflow model to address this challenge. In the proposed model, agent-based technology provides the workflow coordination at both inter- and intra-enterprise levels while Web service-based technology provides infrastructures for messaging, service description and workflow enactment. A proof-of-concept prototype system simulating the order entry, partner search and selection, and contracting in a virtual enterprise creation scenario is implemented to demonstrate the dynamic workflow definition and execution for inter-enterprise collaboration.

title: An autonomous agent development environment for engineering applications
abstract: Agent technology is playing an increasingly important role in developing distributed, intelligent, and collaborative engineering applications. The innate difficulties of interoperation between heterogeneous agent communities and rapid development of multi-agent systems have motivated the emergence of FIPA specifications and the proliferation of multi-agent system platforms or toolkits that implement FIPA specifications. This paper presents a FIPA compliant multi-agent framework called Autonomous Agent Development Environment (AADE). Originating from the engineering fields, AADE can facilitate the rapid development of collaborative engineering applications (especially in engineering design and manufacturing fields) through the provision of reusable packages of agent level components and programming tools. An agent oriented engineering project on the development of a collaborative product design environment is implemented based on the facilities provided by AADE.

title: Community Information Management
abstract: We introduce Cimple, a joint project between the University of Illinois and the University of Wisconsin. Cimple aims to develop a software platform that can be rapidly deployed and customized to manage data-rich online communities. We first describe the envisioned working of such a software platform and our prototype, DBLife, which is a community portal being developed for the database research community. We then describe the technical challenges in Cimple and our solution approach. Finally, we discuss managing uncertainty and provenance, a crucial task in making our software platform practical.

title: Constraint-Based Entity Matching
abstract: Entity matching is the problem of deciding if two given mentions in the data, such as "Helen Hunt" and "H. M. Hunt", refer to the same real-world entity. Numerous solutions have been developed, but they have not considered in depth the problem of exploiting integrity constraints that frequently exist in the domains. Examples of such constraints include "a mention with age two cannot match a mention with salary 200K" and "if two paper citations match, then their authors are likely to match in the same order". In this paper we describe a probabilistic solution to entity matching that exploits such constraints to improve matching accuracy. At the heart of the solution is a generative model that takes into account the constraints during the generation process, and provides well-defined interpretations of the constraints. We describe a novel combination of EM and relaxation labeling algorithms that efficiently learns the model, thereby matching mentions in an unsupervised way, without the need for annotated training data. Experiments on several real-world domains show that our solution can exploit constraints to significantly improve matching accuracy, by 3-12% F-1, and that the solution scales up to large data sets.

title: Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans
abstract: Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations.

title: Source-aware Entity Matching: A Compositional Approach
abstract: Entity matching (a.k.a. record linkage) plays a crucial role in integrating multiple data sources, and numerous matching solutions have been developed. However, the solutions have largely exploited only information available in the mentions and employed a single matching technique. We show how to exploit information about data sources to significantly improve matching accuracy. In particular, we observe that different sources often vary substantially in their level of semantic ambiguity, thus requiring different matching techniques. In addition, it is often beneficial to group and match mentions in related sources first, before considering other sources. These observations lead to a large space of matching strategies, analogous to the space of query evaluation plans considered by a relational optimizer. We propose viewing entity matching as a composition of basic steps into a "match execution plan". We analyze formal properties of the plan space, and show how to find a good match plan. To do so, we employ ideas from social network analysis to infer the ambiguity and related-ness of data sources. We conducted extensive experiments on several real-world data sets on the Web and in the domain of personal information management (PIM). The results show that our solution significantly outperforms current best matching methods.

title: Declarative Information Extraction Using Datalog with Embedded Extraction Predicates
abstract: In this paper we argue that developing information extraction (IE) programs using Datalog with embedded procedural extraction predicates is a good way to proceed. First, compared to current ad-hoc composition using, e.g., Perl or C++, Datalog provides a cleaner and more powerful way to compose small extraction modules into larger programs. Thus, writing IE programs this way retains and enhances the important advantages of current approaches: programs are easy to understand, debug, and modify. Second, once we write IE programs in this framework, we can apply query optimization techniques to them. This gives programs that, when run over a variety of data sets, are more efficient than any monolithic program because they are optimized based on the statistics of the data on which they are invoked. We show how optimizing such programs raises challenges specific to text data that cannot be accommodated in the current relational optimization framework, then provide initial solutions. Extensive experiments over real-world data demonstrate that optimization is indeed vital for IE programs and that we can effectively optimize IE programs written in this proposed framework.