Intelligent jurisprudence research: a new concept

Paper presented at The 7th International Conference on Artificial Intelligence and Law, ICAIL 99: pp. 164-172.Intelligent Jurisprudence Research (IJR) is a concept that consists in performing jurisprudence research with a computational tool that employs Artificial Intelligence (AI) techniques. Jurisprudence research is the search employed by judicial professionals when seeking for past legal situations that may be useful to a legal activity. When humans perform jurisprudence research, they employ analogical reasoning in comparing a given actual situation with past decisions, noting the affinities between them. In the process of remembering a similar situation when faced to a new one, Case-Based Reasoning (CBR) systems simulate analogical reasoning. Therefore, CBR is an appropriate technology to deal with the chosen problem

Reasoning with organizational case bases in the absence of negative exemplars

In: L Lamontagne, J A Recio-Garcia (eds): Proceedings of the ICCBR 2012 Workshops. Lyon, France, 3-6 Sept 2012. p. 35-44.Organizational case bases are gathered based on the organization they serve; cases are not selected taking reasoning into account. Thus, organizational case bases may lack negative exemplars and have multiple solutions to one problem, making it difficult learn weights for reasoning. Case bases in typical Process-Oriented Case-Based Reasoning (POCBR) contexts are organizational, thus inheriting those problems. This paper describes an approach to identify a subset of cases from an organizational case base that meets the criterion that similar problems have similar solutions. This subset is then used to characterize classes, establishing positive and negative exemplars that are then used to learn weights for reasoning with the entire case base. We apply this approach to three organizational case bases, showing significant improvements in accuracy with weights learned with this approach in case bases without negative exemplars

Blueprints for success: Guidelines for building multidisciplinary collaboration teams

From the International Conference on Agents and Artificial Intelligence. J.Filipe and A.L.N.Fred. Vilamoura, Algarve, Portugal, SciTePress. 1: 387-393.Finding collaborators to engage in academic research is a challenging task, especially when the collaboration is multidisciplinary in nature and collaborators are needed from different disciplines. This paper uses evidence of successful multidisciplinary collaborations, funded proposals, in a novel way: as an input for a method of recommendation of multidisciplinary collaboration teams. We attempt to answer two questions posed by a collaboration seeker: what disciplines provide collaboration opportunities and what combinations of characteristics of collaborators have been successful in the past? We describe a two-step recommendation framework where the first step recommends potential disciplines with collaboration potential based on current trends in funding. The second step recommends characteristics for a collaboration team that are consistent with past instances of successful collaborations. We examine how this information source can be used in a case-based recommender system and present a preliminary validation of the system using statistical methods

Intelligent elicitation of military lessons

Paper presented at The Sixth International Conference on Intelligent User Interfaces, San Francisco, CA: pp. 226-227.We introduce LET (Lesson Elicitation Tool), which uses domain and linguistic knowledge to guide users during their submission of lessons learned. LET can detect a user’s need for instructions and disambiguates expressions while collecting taxonomic domain knowledge

Recommending Collaborators for Multidisciplinary Academic Collaboration

The research challenges facing the scientific community have spurred an increase in multidisciplinary research. Such multidisciplinary collaborations span traditional disciplinary boundaries, bringing together researchers with diverse backgrounds, skills, and research practices. With agencies such as the National Science Foundation and National Institutes of Health seeking to encourage this type of research by increasing funding opportunities, this provides incentives for researchers, particularly for tenure track junior faculty, to advance their careers by engaging in multidisciplinary research. To engage in multidisciplinary collaborations, researchers have to find collaborators outside of their domain, a task harder than finding a collaborator within one’s own domain. The personal resources that can be leveraged and the technological tools currently available fall short of meeting the needs of an academic researcher seeking a collaborator with whom to engage in multidisciplinary research. This research explores the possibility of a systematic solution to the problem of finding collaborators in disciplines outside one’s own. One method of problem solving is to use previous successful examples as a guide. Utilizing this type of reasoning in a systematic manner, this research investigates how existing collections of outputs from collaborations, such as grants and peer-reviewed journal publications, can be used to solve this problem of finding partners to engage in multidisciplinary research collaborations. These are collections of collaborations that have achieved some degree of success: each grant proposal was funded and each article was published in a peer-reviewed journal. This research explores whether there is knowledge embedded in these past experiences that can be used to recommend new potential collaborators for those seeking to engage in multidisciplinary research

Knowledge management for computational intelligence systems

Proceedings of the 8th IEEE International Symposium on High Assurance Systems Engineering, HASE 2004, pp. 116–125.Computer systems do not learn from previous experiences unless they are designed for this purpose. Computational intelligence systems (CIS) are inherently capable of dealing with imprecise contexts, creating a new solution in each new execution. Therefore, every execution of a CIS is valuable to be learned. We describe an architecture for designing CIS that includes a knowledge management (KM) framework, allowing the system to learn from its own experiences, and those learned in external contexts. This framework makes the system flexible and adaptable so it evolves, guaranteeing high levels of reliability when performing in a dynamic world. This KM framework is being incorporated into the computational intelligence tool for software testing at National Institute for Systems Test and Productivity. This paper introduces the framework describing the two underlying methodologies it uses, i.e. case-based reasoning and monitored distribution; it also details the motivation and requirements for incorporating the framework into CIS

Representing scientific knowledge

From the Cognition and Exploratory Learning in the Digital Age. Rio de Janeiro, Brazil. 6 - 8 November.A representation for scientific knowledge can enable the computational manipulation of scientific contents. The goal is to facilitate the construction of deliverables in support of education. These learning products can range in a variety of dimensions: in format from textual to computational; in educational content from scientific articles to textbooks; in audience interests, from politicians to the general public; they can be interactive, passive, or proactive. The need to enhance our capacity to build learning products originates from the current multifaceted media context. Educators need to compete for students’ interest with sophisticated forms of web content built with the participation of millions. This work proposes the representation and manipulation of units of scientific knowledge, learning units, to (semi-)automatically manipulate scientific contents to efficiently construct effective learning products. We illustrate the use of learning units for the automated construction of slide presentations

From texts, images, and data to attribute based case representation

Workshop program at the 12th Industrial Conference on Data Mining ICDM 2012 (MLDM 2012) July 13-20, 2012, Berlin/GermanyIn this article we study complex case representations in Case-Based Reasoning. To some degree this is a survey paper. But in addition it gives a unified approach to solving the problems connected with representations mentioned in the title in a way that has not been considered so far. The most popular form to represent cases use attribute-based representations. They allow an easy formulation of similarity measures and retrieval functions. However, in practical applications, case problems and solutions are in the first place given in other ways, e.g. by using texts, images, sensor data or speech data. On this level it is hard to apply reasoning and in particular CBR. This is due to the difficulty to determine similarity measures and retrieval functions. In order to overcome this we introduce a general level structure that allows to bridge the gap between bit-oriented low level and the attribute-oriented high level that is accessible to humans as well as CBR systems. The approach is put in the form of a process model

Information technology incorporating emotion in dialogues

Paper presented at i-Society 2006 Conference, in Miami, FL.Human computer communication is sometimes difficult due to lack of emotion and diversification. At times when many research projects target the development of technology to support people without adult level cognitive abilities, repetitive, and emotionless communication may challenge any chances of success with the new technology. In this paper, we examine a case-based method that relies on the listener’s emotional context to recommend a communication strategy that is both diverse and embedded with relevant motivational aspects