CASE-BASED REASONING IN SOFrWARE EFFORT ESTIMATION

A case-based analogical reasoning model, called Estor, was proposed and elaborated from verbal protocols gathered in a prior study. Estor incorporates five analogical problem solving processes: problem representation analog retrieval, solution transfer, attribute mapping, and no-correspondence adjustment. These five generic processes were supplemented with the domain-specific knowledge of the referent expert. The resulting system was then presented with fifteen software effort estimation tasks, ten of which were among those solved by the referent expert, plus five new tasks. For comparison, the expert was asked to estimate the five new tasks as well. The estimates of Estor were then compared to those of the expert as well as those of the Function Point and COCOMO estimations of the projects. Significant between-estimator differences were found, with the human expert and Estor dominating the effects. Correlations between the actual effort values and the estimates of the expert and Estor for all fifteen projects were .98 and .97 respectively. Furthermore, these coefficients differed significantly from those of COCOMO and Function Points. Differences between the model and the referent expert are discussed

Inter-Organizational Learning in Technology Acquisitions: Procuring More Than Knowledge

The fifth wave of Merger and Acquisition includes an increasing amount of technology acquisitions. Large firms acquire small, technology-centric firms as an external source of knowledge and innovation. A major challenge in these acquisitions is to capture the knowledge of the acquired firm as well as to assimilate and utilize it in the acquiring company. We extend March’s (1991) model of organizational learning through exploration and exploitation with an agent-based model allowing knowledge transfer across organizational boundaries from a target organization to an acquiring organization through (1) retention of employees from the smaller company into the large one or through (2) appropriation of the smaller target company’s organizational code by incorporation of industry best practice or cutting-edge technology. Our preliminary results qualify and extend March’s (1991) conclusions

PERFORMANCE =/= BEHAVIOR: A STUDY IN THE FRAGILITY OF EXPERTISE

The fragility of expertise is a known, but little understood, feature of expert reasoning. Essentially, fragili(y refers to the performance degradation of experts as task properties change. A study is presented in which the fragility of expertise in a complex, real-world task -- reactive scheduling -- is investigated. Six novices (students, trained in the task but with no experience in the domain) and three expert schedulers (ranging from six to 20 years of experience in the domain) each completed six reactive scheduling tasks varying in difficulty. All subjects were run individually and their protocols (verbal and action) were recorded on video-tape. Simple modifications to the task environment were sufficient to degrade the pelfonnance of the experts, sometimes to the level of the novices. However, an analysis of the behavior of the subjects suggests that a problem space characterization of fragility can explain how that degradation occurred. The behavior captured in the video-tapes (both verbal utterances and physical actions) show that, in this task, the primary source of degradation was the inappropriate formation of problem space components. That is, experts were stuck in the wrong problem space. Specifically, the experts would use inadequate search control knowledge while traversing problem spaces and/or repeatedly attempt to implement operators or types of search control knowledge that were not allowed in the experimental task, but were quite valid in the real task setting. We conclude by discussing the concept of expert fragility and how it should be taken into account when designing systems based on the construct of expertise: expert systems

A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department

Objectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs

The price of your soul: neural evidence for the non-utilitarian representation of sacred values

Sacred values, such as those associated with religious or ethnic identity, underlie many important individual and group decisions in life, and individuals typically resist attempts to trade off their sacred values in exchange for material benefits. Deontological theory suggests that sacred values are processed based on rights and wrongs irrespective of outcomes, while utilitarian theory suggests that they are processed based on costs and benefits of potential outcomes, but which mode of processing an individual naturally uses is unknown. The study of decisions over sacred values is difficult because outcomes cannot typically be realized in a laboratory, and hence little is known about the neural representation and processing of sacred values. We used an experimental paradigm that used integrity as a proxy for sacredness and which paid real money to induce individuals to sell their personal values. Using functional magnetic resonance imaging (fMRI), we found that values that people refused to sell (sacred values) were associated with increased activity in the left temporoparietal junction and ventrolateral prefrontal cortex, regions previously associated with semantic rule retrieval. This suggests that sacred values affect behaviour through the retrieval and processing of deontic rules and not through a utilitarian evaluation of costs and benefits

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. 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Learning capability : the effect of existing knowledge on learning

It has been observed that different people learn the same things in different ways - increasing their knowledge of the subject/domain uniquely. One plausible reason for this disparity in learning is the difference in the existing personal knowledge held in the particular area in which the knowledge increase happens. To understand this further, in this paper knowledge is modelled as a 'system of cognitive schemata', and knowledge increase as a process in this system; the effect of existing personal knowledge on knowledge increase is 'the Learning Capability'. Learning Capability is obtained in form of a function; although it is merely a representation making use of mathematical symbolism, not a calculable entity. The examination of the function tells us about the nature of learning capability. However, existing knowledge is only one factor affecting knowledge increase and thus one component of a more general model, which might additionally include talent, learning willingness, and attention