A formalism and method for representing and reasoning with process models authored by subject matter experts

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic in order to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82% were well-formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25% and 30% with respect to the base case, respectively

A framework and computer system for knowledge-level acquisition, representation, and reasoning with process knowledge

The development of knowledge-based systems is usually approached through the combined skills of software and knowledge engineers (SEs and KEs, respectively) and of subject matter experts (SMEs). One of the most critical steps in this task aims at transferring knowledge from SMEs’ expertise to formal, machine-readable representations, which allow systems to reason with such knowledge. However, this process is costly and error prone. Alleviating such knowledge acquisition bottleneck requires enabling SMEs with the means to produce the target knowledge representations, minimizing the intervention of KEs. This is especially difficult in the case of complex knowledge types like processes. The analysis of scientific domains like Biology, Chemistry, and Physics uncovers: (i) that process knowledge is the single most frequent type of knowledge occurring in such domains and (ii) specific solutions need to be devised in order to allow SMEs to represent it in a computational form. We present a framework and computer system for the acquisition and representation of process knowledge in scientific domains by SMEs. We propose methods and techniques to enable SMEs to acquire process knowledge from the domains, to formally represent it, and to reason about it. We have developed an abstract process metamodel and a library of problem solving methods (PSMs), which support these tasks, respectively providing the terminology for SME-tailored process diagrams and an abstract formalization of the strategies needed for reasoning about processes. We have implemented this approach as part of the DarkMatter system and formally evaluated it in the context of the intermediate evaluation of Project Halo, an initiative aiming at the creation of question answering systems by SMEs

An exact approach for single machine scheduling with quadratic earliness and tardiness penalties

In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose two different lower bounds, as well as a lower bounding procedure that combines these two bounds. Optimal branch-and-bound algorithms are then presented. These algorithms incorporate the proposed lower bound, as well as an insertion-based dominance test. The lower bounding procedure and the branch-and-bound algorithms are tested on a wide set of randomly generated problems. The computational results show that the branch-and-bound algorithms are capable of optimally solving, within reasonable computation times, instances with up to 20 jobs.scheduling, single machine, quadratic earliness and tardiness, lower bounds, branch-and-bound

Beyond the Formalism Debate: Expert Reasoning, Fuzzy Logic, and Complex Statutes

Formalists and antiformalists continue to debate the utility of using legislative history and current social values to interpret statutes. Lost in the debate, however, is a clear model of how judges actually make decisions. Rather than focusing on complex problems presented by actual judicial decisions, formalists and antiformalists concentrate on stylized examples of simple statutes. In this Article, Professors Adams and Farber construct a more functional model of judicial decisionmaking by focusing on complex problems. They use cognitive psychological research on expert reasoning and techniques from an emerging area in the field of artificial intelligence, fuzzy logic, to construct their model. To probe the complex interactions between judicial interpretation, the business and legal communities, and the legislature, the authors apply their model to two important bankruptcy cases written by prominent formalist judges. Professors Adams and Farber demonstrate how cognitive psychology and fuzzy logic can reveal the reasoning processes that both formalist and antiformalist judges use to interpret \u27complex statutes. To apply formalist rules, judges need to recognize the aspects of a case that trigger relevant rules. Cognitive psychologists have researched expert reasoning using this type of diagnostic process. Once the judge identifies the appropriate rules, she will often find they point in conflicting directions. Fuzzy logic provides a model of how to analyze such conflicts. Next, Professors Adams and Farber consider how these models of judicial decisionmaking inform efforts to improve statutory interpretation of complex statutes. They reason that expert decisionmaking builds on pattern recognition skills and fuzzy maps, both the result of intensive repeated experience. The authors explain that cases involving complex statutory interpretation frequently involve competing considerations, and that the implicit understandings of field insiders tend to be entrenched and difficult to displace. Consequently, Professors Adams and Farber argue that judges in specialty courts, such as the Bankruptcy Courts, are probably in a better position than generalist appellate judges to interpret complex statutes. Generalist judges should approach complex statutory issues with a strong degree of deference to the local culture of the field. Professors Adams and Farber conclude the Article with speculation on how fuzzy logic could be used in a more quantitative way to model legal problems. They note that computer modeling may ultimately provide insight into the subtle process of judicial practical reasoning, moving away from the false dichotomy often drawn between formalist and antiformalist approaches to practical judicial decision- making

Beyond the Formalism Debate: Expert Reasoning, Fuzzy Logic, and Complex Statutes

Formalists and antiformalists continue to debate the utility of using legislative history and current social values to interpret statutes. Lost in the debate, however, is a clear model of how judges actually make decisions. Rather than focusing on complex problems presented by actual judicial decisions, formalists and antiformalists concentrate on stylized examples of simple statutes.In this Article, Professors Adams and Farber construct a more functional model of judicial decisionmaking by focusing on complex problems. They use cognitive psychological research on expert reasoning and techniques from an emerging area in the field of artificial intelligence, fuzzy logic, to construct their model. To probe the complex interactions between judicial interpretation, the business and legal communities, and the legislature, the authors apply their model to two important bankruptcy cases written by prominent formalist judges.Professors Adams and Farber demonstrate how cognitive psychology and fuzzy logic can reveal the reasoning processes that both formalist and antiformalist judges use to interpret complex statutes. To apply formalist rules, judges need to recognize the aspects of a case that trigger relevant rules. Cognitive psychologists have researched expert reasoning using this type of diagnostic process. Once the judge identifies the appropriate rules, she will often find they point in conflicting directions. Fuzzy logic provides a model of how to analyze such conflicts.Next, Professors Adams and Farber consider how these models of judicial decisionmaking inform efforts to improve statutory interpretation of complex statutes. They reason that expert decisionmaking builds on pattern recognition skills and fuzzy maps, both the result of intensive repeated experience. The authors explain that cases involving complex statutory interpretation frequently involve competing considerations, and that the implicit understandings of field insiders tend to be entrenched and difficult to displace. Consequently, Professors Adams and Farber argue that judges in specialty courts, such as the Bankruptcy Courts, are probably in a better position than generalist appellate judges to interpret complex statutes. Generalist judges should approach complex statutory issues with a strong degree of deference to the local culture of the field.Professors Adams and Farber conclude the Article with speculation on how fuzzy logic could be used in a more quantitative way to model legal problems. They note that computer modeling may ultimately provide insight into the subtle process of judicial practical reasoning, moving away from the false dichotomy often drawn between formalist and antiformalist approaches to practical judicial decisionmaking. Formalist, Antiformalist, Fuzzy Logic, Statutory Interpretatio

Authoring knowledge based tutors: ‘tools for content, instructional strategy, student model, and interface design

Abstract While intelligent tutoring systems (ITSs), also called knowledge based tutors, are becoming more common and proving to be increasingly effective, each one must still be built from scratch at a significant cost. We have developed domain independent tools for authoring all aspects of a knowledge based tutor: the domain model, the teaching strategies, the student model, and the learning environment. In this paper we describe these tools, discuss a number of design issues and design tradeoffs that are involved in building ITS authoring tools, and discuss knowledge acquisition and representation issues encountered in our work. We also describe how we plan to use these tools (collectively called Eon), including "ontology objects," as a meta-authoring tool for designing special purpose authoring tools tailored for specific domain types

Computable Rationality, NUTS, and the Nuclear Leviathan

This paper explores how the Leviathan that projects power through nuclear arms exercises a unique nuclearized sovereignty. In the case of nuclear superpowers, this sovereignty extends to wielding the power to destroy human civilization as we know it across the globe. Nuclearized sovereignty depends on a hybrid form of power encompassing human decision-makers in a hierarchical chain of command, and all of the technical and computerized functions necessary to maintain command and control at every moment of the sovereign's existence: this sovereign power cannot sleep. This article analyzes how the form of rationality that informs this hybrid exercise of power historically developed to be computable. By definition, computable rationality must be able to function without any intelligible grasp of the context or the comprehensive significance of decision-making outcomes. Thus, maintaining nuclearized sovereignty necessarily must be able to execute momentous life and death decisions without the type of sentience we usually associate with ethical individual and collective decisions