System of experimental design : engineering methods to optimize quality and minimize costs

Methods and apparatus, including computer program products, for interacting with a user to define business rules in a declarative manner. A rule set is displayed (FIG. 6, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622) as an editable list of conditions (606) and an editable list of actions (608), the conditions and actions being linked to each other by the combination of an editable list of if-values (612) and an editable list of then-values (614). If-values and then-values are explicitly linked to each other, conditions and if-values are explicitly linked to each other, and then-values and actions are explicitly linked to each other in the displayed lists. Inputs can be received from a user editing one or more of the editable lists. In particular implementations, the editable lists are displayed in a matrix structure of four quadrants (606, 608, 612, 614). Adjacency of if-values and then-values is used to represent a rule.

Business rules user interface for development of adaptable enterprise applications

Agent-based modeling is an object-oriented, discrete event, population-focused method for the computational representation of dynamic systems. Agent-based models (ABMs) treat systems as aggregates of populations of interacting components governed by rules. This means of system representation allows ABMs to map well to how biological knowledge is represented and communicated. As a result, agent-based modeling is an intuitive means by which biomedical researchers can represent their knowledge in a dynamic computational form and in so doing can lower the threshold for the general biological researcher to engage in computational modeling. ABMs are particularly suited for representing the behavior of populations of cells (i.e., “cell-as-agents”) but have also been used to model lower level processes, such as molecular interactions when spatial and structural properties are involved, as well as higher level systems, such as in human populations in epidemiological studies. For purposes of its use in translational systems biology, we focus on the use of cell/tissue-as-agent ABMs and demonstrate how agent-based modeling can serve as an integrating framework for dynamic knowledge representation of biological systems.

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Agent-based models in translational systems biology

A Qualitative Physics Based on Confluences

The use of "levels of abstraction" in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section "Some Definitions and Preliminary Examples" the nature and applicability of the epistemological method of levels of abstraction is clarified. In section "A Classic Application of the Method of Abstraction", the philosophical fruitfulness of the new method is shown by using Kant's classic discussion of the "antinomies of pure reason" as an example. In section "The Philosophy of the Method of Abstraction", the method is further specified and supported by distinguishing it from three other forms of "levelism": (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems.

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The Method of Levels of Abstraction

Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach.

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Using ontologies for simulation modeling

Characterizes the problem of multiple levels of abstraction in simulation modeling and develops an approach that addresses the problem. In this paper, we describe: (i) the notion of abstraction and the technical problems associated with multiple levels of abstraction, (ii) how abstractions affect different activities during the simulation modeling process, (iii) a preliminary approach for addressing the problems associated with multiple levels of abstraction, (iv) the conceptual architecture of a simulation modeling environment that implements the proposed approach, and (v) a summary of the research on questions of abstraction in simulation.

Simulation modeling at multiple levels of abstraction

Current business modeling methods are one-dimensional. Here, an integrated modeling approach helps decision makers study the enterprise as a whole from multiple perspectives.

Integrated modeling: the key to holistic understanding of the enterprise

An important requirement for world-class CIM systems is the ability to capture knowledge from multiple disciplines and store it in a form that facilitates re-use, sharing, and extendibility. Taxonomies and glossaries, in and of themselves, will not fully address this requirement, and will need to be supplemented so as to circumscribe the meanings and logical properties of the terms as precisely as possible. We thus perceive the need for ontologies rather than mere taxonomies. An ontology is a description of the kinds of things, both physical and conceptual, that make up a given domain and the relationships among them as represented by the terminology in that domain. This paper describes a scientific method for acquiring, structuring and maintaining CIM ontologies. An ontology capture method is essential to developing practical CIM ontologies because it facilitates the direct capture of CIM knowledge by practitioners within the manufacturing domain. The proposed ontology capture method includes: (...

Toward a method for acquiring CIM ontologies

This paper describes an approach to design systems using computer simulation experiments. The distinguishing feature of the approach is to develop a strategy that considers system robustness as a major design criterion. Motivated by Taguchi's methods to design robust products, our research investigates the utility of this approach to design robust systems. Explicit consideration of robustness upfront in the design process represents a major paradigm shift for system design methodologies. Consequently, the results of our research have the poten tial to significantly impact future simulation- based system design methods.

Perakath C. Benjamin

Papers

Using ontologies for simulation modeling

Simulation modeling at multiple levels of abstraction

Integrated modeling: the key to holistic understanding of the enterprise

Toward a method for acquiring CIM ontologies

Using Simulation for Robust System Design