[Show abstract][Hide abstract] ABSTRACT: This article describes an investigation into the feasibility of using contextual reasoning to monitor and supervise the collaborative work of several knowledge workers working together on a project. Managing large and complex projects is a difficult task that requires
by the project manager to be able to be proactive when possible and to react correctly in the presence of events. In complex projects, effective oversight of the project personnel and the progress of the project are essential in ensuring that project objectives are met. This is especially true of projects that require contributions from various experts, whose interaction may be limited to a Web-based collaborative tool. Such oversight is typically the job of a project manager who is tasked with avoiding cost overruns, shipment delays, and ensuring product effectiveness. We utilize
as the tools of choice for implementing an agent that emulates the function of a competent project manager. We use rocket design and manufacture as the domain to evaluate our technique. We use a public domain rocket design software package developed by the National Aeronautics and Space Administration as a guide to the domain. The article describes the investigation, its results, and the related works in a collaborative design project.
Artificial intelligence for engineering design analysis and manufacturing 02/2011; 25(01):25-40. DOI:10.1017/S0890060410000156 · 0.60 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In order to make CSCW (Computer Supported Cooperative Works) more intelligent and dependable, the usefulness of contextual reasoning is discussed in this paper. To ensure the project objectives are met, a project manager (PM) maintaining control of the project plays much important roles in complex projects where interaction may be limited to a web-based collaborative tool. In such limitations, he should more strongly help avoid cost overruns, shipment delays, but most importantly, product performance including product reliability. We utilize Context-based Reasoning (CxBR) for implementing such control measures as typically used by competent PMs. A rocket development project is used as the domain to evaluate our technique, using NASA’s open software for rocket design. Through the experimental evaluation, the easiness of validation and refinement of Knowledge represented by CxBR is clarified. As well, this Knowledge representation is expected to be effective even for building a web-based complex and intelligent CSCW system where biological sensor fusions are included for situational assessment to aim at the further reliable collaboration support that can avoid such as misunderstanding, mishearing, etc.
[Show abstract][Hide abstract] ABSTRACT: This paper describes a research project that investigated the feasibility of using contextual reasoning to supervise the collaborative work of knowledge workers. In complex projects that require contributions from various experts but whose interaction may be limited to a web-based collaborative tool, proper management of the project is essential to ensure that the project objectives are met. This is typically the job of a project manager. We assert that having situational awareness is likewise essential to managing a project, and we utilize Context-based Reasoning (CxBR) as the tool of choice for implementing situational awareness in an agent that assists project managers. We use rocket design and manufacture as the domain to evaluate our approach. We make use of public domain rocket design software developed by NASA as a guide to the domain. The paper describes the investigation and the related works involved in collaborative design project, as embodied by designing and building a small rocket.
Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference, May 15-17, 2008, Coconut Grove, Florida, USA; 01/2008
[Show abstract][Hide abstract] ABSTRACT: We report on a study in which twelve different paradigms were used to implement agents acting in an environment which borrows elements from artificial life and multi-player strategy games. In choosing the paradigms we strived to maintain a balance between high level, logic based approaches and low level, physics oriented models; between imperative programming, declarative approaches and “learning from basics”; between anthropomorphic or biologically inspired models on one hand and pragmatic, performance oriented approaches on the other. We have found that the choice of the paradigm determines the software development process and requires a different set of skills from the developers. In terms of raw performance, we found that the best performing paradigms were those which (a) allowed the knowledge of human experts to be explicitly transferred to the agent and (b) allowed the integration of well-known, high performance algorithms. We have found that maintaining a commitment to the chosen paradigm can be difficult; there is a strong temptation to offer shallow fixes to perceived performance problems through a “flight into heuristics”. Our experience is that a development process without the discipline enforced by a central paradigm leads to agents which are a random collection of heuristics whose interactions are not clearly understood. Although far from providing a definitive verdict on the benefits of the different paradigms, our study provided a good insight into what kind of conceptual, technical or organizational problems would a development team face depending on their choice of agent paradigm. I.
[Show abstract][Hide abstract] ABSTRACT: We report on a study in which twelve dierent paradigms were used to implement agents acting in an environment which borrows elements from artificial life and multi-player strategy games. In choosing the paradigms we strived to maintain a balance between high level, logic based approaches to low level, physics oriented models; between impera- tive programming, declarative approaches and "learning from basics" as well as between anthropomorphic or biologically inspired models on one hand and pragmatic, performance oriented approaches on the other. Instead of strictly numerical comparisons (which can be applied to cer- tain pairs of paradigms, but might be meaningless for others), we had chosen to view each paradigm as a methodology, and compare the de- sign, development and debugging process of implementing the agents in the given paradigm. We found that software engineering techniques could be easily applied to some approaches, while they appeared basically meaningless for other ones. The performance of some agents were easy to predict from the start of the development, for other ones, impossible. The eort re- quired to achieve certain functionality varied widely between the dierent paradigms. Although far from providing a definitive verdict on the ben- efits of the dierent paradigms, our study provided a good insight into what type of conceptual, technical or organizational problems would a development team face depending on their choice of agent paradigm.
Programming Multi-Agent Systems, 4th International Workshop, ProMAS 2006, Hakodate, Japan, May 9, 2006, Revised and Invited Papers; 01/2006
[Show abstract][Hide abstract] ABSTRACT: Decision support systems that capture, preserve, and reuse implicit knowledge can greatly benefit from explicitly using context. The development of this type of system can benefit from a context-based knowledge representation paradigm to be effective in real-world applications. This paper describes how the implementation of the contextual graphs formalism in the AlexDSS system addresses the system's use of context. Contextual graph's explicit use of context combats the common user interaction problems of irrelevancy and redundancy that plague knowledge-based systems. The development of AlexDSS supports the notion that the contextual graph's formalism is a viable solution for a real- world decision support system. This paper discusses how contextual graphs are employed in decision support systems.
Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, Melbourne Beach, Florida, USA, May 11-13, 2006; 01/2006