Concrete is the most widely used engineered material in the world with more than 10 billion tons produced annually. Unfortunately, with that scale comes a significant burden in terms of energy, water, and release of greenhouse gases and other pollutants; indeed 8% of worldwide carbon emissions are attributed to the production of cement, a key ingredient in concrete. As such, there is interest in creating concrete formulas that minimize this environmental burden, while satisfying engineering performance requirements including compressive strength. Specifically for computing, concrete is a major ingredient in the construction of data centers.

The complex process of adapting pre-packaged applications, such as Oracle or SAP, to an organization's needs is full of challenges. Although detailed, structured, and well-documented methods govern this process, the consulting team implementing the method must spend a huge amount of manual effort to make sure the guidelines of the method are followed as intended by the method author. MATCON breaks down the method content, documents, templates, and work products into reusable objects, and enables them to be cataloged and indexed so these objects can be easily found and reused on subsequent projects. By using models and meta-modeling the reusable methods, we automatically produce a CASE tool to apply these methods, thereby guiding consultants through this complex process. The resulting tool helps consultants create the method deliverables for the initial phases of large customization projects. Our MATCON output, referred to as Consultant Assistant, has shown significant savings in training costs, a 20 - 30% improvement in productivity, and positive results in large Oracle and SAP implementations.

Mashup editors, like Yahoo Pipes and IBM Lotus Mashup Maker, allow non-programmer end-users to ldquomash-uprdquo information sources and services to meet their information needs. However, with the increasing number of services, information sources and complex operations like filtering and joining, even an easy to use editor is not sufficient. MashupAdvisor aims to assist mashup creators to build higher quality mashups in less time. Based on the current state of a mashup, the MashupAdvisor quietly suggests outputs (goals) that the user might want to include in the final mashup. MashupAdvisor exploits a repository of mashups to estimate the popularity of specific outputs, and makes suggestions using the conditional probability that an output will be included, given the current state of the mashup. When a suggestion is accepted, MashupAdvisor uses a semantic matching algorithm and a metric planner to modify the mashup to produce the suggested output. Our prototype was implemented on top of IBM Lotus MashupMaker and our initial results show that it is effective.

Automatically finding suitable Web services given a request is a difficult problem because the interface descriptions of Web services are often terse and cryptic. Dictionary and information retrieval based techniques have proven useful in disambiguating the semantics of service descriptions, but they are limited in their capability to consider the relationships between the words describing the Web services. Current ontology-based approaches typically require a user to explicitly create domain ontologies. This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services. Our approach differs from earlier work on service matching by considering the relationship between words rather than treating them as a bag of unrelated words. The experimental results indicate that with our unsupervised approach we can eliminate up to 70% of incorrect matches that are made by dictionary-based approaches.

This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services.

In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semi-automated composition tools or directory browsers

In this paper, we explore the use of domain-independent and domain-specific ontologies to find matching service descriptions. The domain-independent relationships are derived using an English thesaurus after tokenization and part-of-speech tagging. The domain-specific ontological similarity is derived by an inference on the semantic annotations associated with Web service descriptions. Matches due to the two cues are combined to determine an overall semantic similarity score. By combining multiple cues, we show that better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.

As an industry-backed registry for Web Services, UDDI plays an important role in helping requesters find suitable services. Unfortunately, the current search functions in UDDI are limited in their support for making automatic service selection decisions. While some approaches have been suggested to enhance the search capabilities in UDDI using service semantics, they suffer from limitations. These approaches either prescribe performing semantic matching outside of UDDI leaving its search function unchanged or propose embedding a specific matching engine within UDDI thereby making the search function inflexible. In this work, we present a flexible mechanism to enhance UDDI search function. Using our approach, users can integrate multiple external matching services with a UDDI registry to support multiple external service description languages. The result is a UDDI registry with flexible and intelligent service search function that can be used for dynamic service selection.

The advent of Web services has made automated workflow composition relevant to Web based applications. One technique, that has received some attention, for automatically composing workflows is AI-based classical planning. However, classical planning suffers from the paradox of first assuming deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services. To address these concerns, we propose using Markov decision processes (MDPs), to model workflow composition. Our method models both, the inherent stochastic nature of Web services, and the dynamic nature of the environment. The resulting workflows are robust to nondeterministic behaviors of Web services and adaptive to a changing environment. Using an example scenario, we demonstrate our method and provide empirical results in its support.

Evolutionary population-based search methods are often used to find a Pareto-optimal set of solutions for hard multicriteria optimization problems. We utilize one such agent architecture to evolve good solution sets to these problems, deploying agents to progressively add, modify and delete candidate solutions in one or more populations over time. Here we describe how we assign neural nets to aid agent decision-making and encourage cooperation to improve convergence to good Pareto optimal solution sets. This paper describes the design and results of this approach and suggests paths for further study.

Reliable navigation is critical for a lunar rover, both for autonomous traverses and safeguarded remote teleoperation. This paper describes an implemented system that has autonomously driven a prototype wheeled lunar rover over a kilometer in natural, outdoor terrain. The navigation system uses stereo terrain maps to perform local obstacle avoidance, and arbitrates steering recommendations from both the user and the rover. The paper describes the system architecture, each of the major components, and the experimental results to date.

There is a wide gulf between the formal logics used by logicians to describe agents and the informal vocabulary used by people who actually build robotic agents. In an effort to help bridge the gap, this paper applies techniques borrowed from the field of formal software methods to develop a common vocabulary. Terms useful for discussing agents are given formal definitions. A framework for describing agents, tasks and environments is developed using the Z specification language. The terms successful, capable, perceptive, predictive, interpretive, rational and sound are then defined relative to this framework. The aims of this paper are to develop a precise, common vocabulary for discussing agents and to provide a basis for rational design of agents.