An approach to legal reasoning based on a hybrid decision-support system

Pal, Kamalendu

doi:10.1016/s0957-4174(99)00015-9

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…In such situations it is appropriate to handle uncertainty by fuzzy set theory (Kacprzyk and Yager, 1985). Some research on the creation of analytic hierarchic decision models based on fuzzy logic has been undertaken recently (Cheng, Young and Hwang, 1999;Chen and Chiou, 1999;Pal, 1999;Radojevic and Petrovic, 1997;Pedrycz, 1996). It is mainly concentrated on the application of AHP and some attempts to avoid subjective judgment in processes that do not relate to personnel selection.…”

Section: Theoretical Foundation Of the Model Formationmentioning

confidence: 99%

“…The popularity of fuzzy logic in decision-making processes is spreading even to legal systems. Pal (1999) applies a model of case similarity assessment based on fuzzy proximity relations for legal decision-making processes. Radojevic and Petrovic (1997) propose`fuzzy set theory and approximate reasoning as an appropriate framework to imitate human reasoning in expressing a preference structure'.…”

Section: Theoretical Foundation Of the Model Formationmentioning

confidence: 99%

“…Combined with fuzzy logic numbers and multi-objective decision models it may help to avoid the problems of individual judgment (Liang and Wang, 1992;Pedrycz, 1996;Cheng, Young andHwang, 1999, Chen andChiou, 1999;Pal, 1999;Radojevic and Petrovic 1997).…”

Section: Introductionmentioning

confidence: 99%

“…In order to avoid`gut feeling' in a personnel selection decision-making procedure the analytic hierarchy process (AHP) has been introduced (Saaty, 1995;Lipovetsky, 1996;Labib, Williams and O'Connor, 1998;Iwamura and Lin, 1998). Combined with fuzzy logic numbers and multi-objective decision models it may help to avoid the problems of individual judgment (Liang and Wang, 1992;Pedrycz, 1996;Cheng, Young andHwang, 1999, Chen andChiou, 1999;Pal, 1999;Radojevic and Petrovic 1997).…”

Section: Introductionmentioning

confidence: 99%

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Personnel Selection Fuzzy Model

Petrovic-Lazarevic

2001

Int Trans Operational Res

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The paper presents a two-level personnel selection fuzzy model: short list and hiring decision. The model is an attempt to minimize subjective judgment in the process of distinguishing between an appropriate employee and an inappropriate employee for a job vacancy. The model comprises an analytic hierarchy process of three levels. The lowest level relates to the preliminary selection or shortlist procedure. Modifying multi-objective models of decision-making, the main decision elements are assumed as linguistic fuzzy variables. The problem is considered broad, since the worth values of the variables are calculated as expected values of the fuzzy variables. The second level relates to the hiring decision or selection of a ®nal candidate for an employment opportunity. The selector assesses his/her own expectations of the short-listed job applicants. The expectations are treated by a probabilistic±possibilistic approach. The top level is the expected utility of hiring the successful candidate. Compared to the traditional way of selecting an appropriate short-listed job applicant this model minimizes individual judgment at both short-listed and hiring decision levels. The model is illustrated by a case study.

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Section: Theoretical Foundation Of the Model Formationmentioning

confidence: 99%

Section: Theoretical Foundation Of the Model Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Personnel Selection Fuzzy Model

Petrovic-Lazarevic

2001

Int Trans Operational Res

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“…Indeed, attempts in the direction of constructing argumentation-based recommender systems have been made by Teze et al (2015). Finally, law is an important domain where expert systems may greatly benefit from AM, with the goal of automatically extracting knowledge from legal documents (Pal, 1999;Galgani et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

MARGOT: A web server for argumentation mining

Lippi

Torroni

2016

Expert Systems with Applications

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Argumentation mining is a recent challenge concerning the automatic extraction of arguments from unstructured textual corpora. Argumentation mining technologies are rapidly evolving and show a clear potential for application in diverse areas such as recommender systems, policy-making and the legal domain. There is a long-recognised need for tools that enable users to browse, visualise, search, and manipulate arguments and argument structures. There is, however, a lack of widely accessible tools. In this article we describe the technology behind MARGOT, the first online argumentation mining system designed to reach out to the wider community of potential users of these new technologies. We evaluate its performance and discuss its possible application in the analysis of content from various domains

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A systematic review and taxonomy of explanations in decision support and recommender systems

Nunes

Jannach

2017

User Model User-Adap Inter

279

193

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With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems.A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today's increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advicegiving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.

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An approach to legal reasoning based on a hybrid decision-support system

Cited by 14 publications

References 10 publications

Personnel Selection Fuzzy Model

Personnel Selection Fuzzy Model

MARGOT: A web server for argumentation mining

A systematic review and taxonomy of explanations in decision support and recommender systems

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