Linking objective and subjective modeling in engineering design through arc-elastic dominance

Engineering design in mechanics is a complex activity taking into account both objective modeling processes derived from physical analysis and designers’ subjective reasoning. This paper introduces arc-elastic dominance as a suitable concept for ranking design solutions according to a combination of objective and subjective models. Objective models lead to the aggregation of information derived from physics, economics or eco-environmental analysis into a performance indicator. Subjective models result in a confidence indicator for the solutions’ feasibility. Arc-elastic dominant design solutions achieve an optimal compromise between gain in performance and degradation in confidence. Due to the definition of arc-elasticity, this compromise value is expressive and easy for designers to interpret despite the difference in the nature of the objective and subjective models. From the investigation of arc-elasticity mathematical properties, a filtering algorithm of Pareto-efficient solutions is proposed and illustrated through a design knowledge modeling framework. This framework notably takes into account Harrington’s desirability functions and Derringer’s aggregation method. It is carried out through the re-design of a geothermal air conditioning system

Improvement of retrieval in Case-Based Reasoning for system design

The problematic addressed in this article is dealing with the improvement of retrieval in Case-Based Reasoning for system design. The retrieval activity is based on the evaluation of similarities between requirements (target) and the solutions (sources). However, similarities between features is often a subjective kind of knowledge difficult to formalize within companies. Based on an ontology of domain, the approach permits to retrieve compatible solutions rather than similar ones using a model of designer preferences. The requirements are modeled by means of constraints. When constraints are confronted to solutions in order to evaluate a compatibility measure, missing information within solutions with regard to requirements are taken into account using semantic similarities between concepts. A case study validates the proposals

Facing Openness with Socio Cognitive Trust and Categories.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments

Abduction for (non-ominiscient) agents

Among the non-monotonic reasoning processes, abduction is one of the most important. Usually described as the process of looking florexplantions, it has been recognized as one of the most commonly used in our daily activities. Still, the traditional definitions of an abductive problem and an abductive solution mention only theories and formulas, leaving agency out of the picture. Our work proposes a study of abductive reasoning from an epistemic and dynamic perspective, making special emphasis on non-ideal agents. We begin by exploring what an abductive problema is in terms of an agent’s information, and what an abductive solution is in terms of the actions that modify it. Then we explore the different kinds of abductive problems and abductive solutions that arise when we consider agents whose information is not closed under logical consequence, and agents whose reasoning abilities are not complete

Influence factors for local comprehensibility of process models

The main aim of this study is to investigate human understanding of process models and to develop an improved understanding of its relevant influence factors. Aided by assumptions from cognitive psychology, this article attempts to address specific deductive reasoning difficulties based on process models. The authors developed a research model to capture the influence of two effects on the cognitive difficulty of reasoning tasks: (i) the presence of different control-flow patterns (such as conditional or parallel execution) in a process model and (ii) the interactivity of model elements. Based on solutions to 61 different reasoning tasks by 155 modelers, the results from this study indicate that the presence of certain control-flow patterns influences the cognitive difficulty of reasoning tasks. In particular, sequence is relatively easy, while loops in a model proved difficult. Modelers with higher process modeling knowledge performed better and rated subjective difficulty of loops lower than modelers with lower process modeling knowledge. The findings additionally support the prediction that interactivity between model elements is positively related to the cognitive difficulty of reasoning. Our research contributes to both academic literature on the comprehension of process models and practitioner literature focusing on cognitive difficulties when using process models

Embedding expert systems in semi-formal domains : examining the boundaries of the knowledge base

This thesis examines the use of expert systems in semi-formal domains. The research identifies the main problems with semi-formal domains and proposes and evaluates a number of different solutions to them. The thesis considers the traditional approach to developing expert systems, which sees domains as being formal, and notes that it continuously faces problems that result from informal features of the problem domain. To circumvent these difficulties experience or other subjective qualities are often used but they are not supported by the traditional approach to design. The thesis examines the formal approach and compares it with a semiformal approach to designing expert systems which is heavily influenced by the socio-technical view of information systems. From this basis it examines a number of problems that limit the construction and use of knowledge bases in semi-formal domains. These limitations arise from the nature of the problem being tackled, in particular problems of natural language communication and tacit knowledge and also from the character of computer technology and the role it plays. The thesis explores the possible mismatch between a human user and the machine and models the various types of confusion that arise. The thesis describes a number of practical solutions to overcome the problems identified. These solutions are implemented in an expert system shell (PESYS), developed as part of the research. The resulting solutions, based on non-linear documents and other software tools that open up the reasoning of the system, support users of expert systems in examining the boundaries of the knowledge base to help them avoid and overcome any confusion that has arisen. In this way users are encouraged to use their own skills and experiences in conjunction with an expert system to successfully exploit this technology in semi-formal domains

Degrees of Belief as Basis for Scientific Reasoning?

Bayesianism is the claim that scientific reasoning is\ud probabilistic, and that probabilities are adequately interpreted\ud as an agent"s actual subjective degrees of belief\ud measured by her betting behaviour.\ud Confirmation is one important aspect of scientific reasoning.\ud The thesis of this paper is the following: Given that\ud scientific reasoning (and thus confirmation) is at all\ud probabilistic, the subjective interpretation of probability has\ud to be given up in order to get right confirmation, and thus\ud scientific reasoning in general

Geometric reasoning via internet crowdsourcing

The ability to interpret and reason about shapes is a peculiarly human capability that has proven difficult to reproduce algorithmically. So despite the fact that geometric modeling technology has made significant advances in the representation, display and modification of shapes, there have only been incremental advances in geometric reasoning. For example, although today's CAD systems can confidently identify isolated cylindrical holes, they struggle with more ambiguous tasks such as the identification of partial symmetries or similarities in arbitrary geometries. Even well defined problems such as 2D shape nesting or 3D packing generally resist elegant solution and rely instead on brute force explorations of a subset of the many possible solutions. Identifying economic ways to solving such problems would result in significant productivity gains across a wide range of industrial applications. The authors hypothesize that Internet Crowdsourcing might provide a pragmatic way of removing many geometric reasoning bottlenecks.This paper reports the results of experiments conducted with Amazon's mTurk site and designed to determine the feasibility of using Internet Crowdsourcing to carry out geometric reasoning tasks as well as establish some benchmark data for the quality, speed and costs of using this approach.After describing the general architecture and terminology of the mTurk Crowdsourcing system, the paper details the implementation and results of the following three investigations; 1) the identification of "Canonical" viewpoints for individual shapes, 2) the quantification of "similarity" relationships with-in collections of 3D models and 3) the efficient packing of 2D Strips into rectangular areas. The paper concludes with a discussion of the possibilities and limitations of the approach