Multi-Criteria Decision Making (MCDM) methods have evolved to accommodate various types of applications. Dozens of methods have been developed, with even small variations to existing methods causing the creation of new branches of research. This paper performs a literature review of common Multi-Criteria Decision Making methods, examines the advantages and disadvantages of the identified methods, and explains how their common applications relate to their relative strengths and weaknesses. The analysis of MCDM methods performed in this paper provides a clear guide for how MCDM methods should be used in particular situations.

/pdf/an-analysis-of-multi-criteria-decision-making-methods-1hzhqosczn.pdf

An Analysis of Multi-Criteria Decision Making Methods

L'etude des technologies sur le lieu de travail constitue une branche des sciences sociales qui peut permettre de mieux comprendre l'impact des technologies de l'information sur les activites organisationnelles, de soutenir la prise de decision, de resoudre les echecs de l'introduction de nouvelles technologies et de contribuer a la conception des systemes. Deux cas sont presentes ici : la facilitation de la collaboration entre employes, qu'ils soient ou non geographiquement distants ; la sous-utilisation des employes et les problemes qui en decoulent. L'etude des technologies sur le lieu de travail est complementee par d'autres approches comme l'informatique sociale, le travail cooperatif assiste par ordinateur et l'ingenierie des exigences des utilisateurs.

Workplace studies and technological change

Business data mining-a machine learning perspective

The Internet is a giant semiotic system. It is a massive collection of Peirce’s three kinds of signs: icons, which show the form of something; indices, which point to something; and symbols, which represent something according to some convention. But current proposals for ontologies and metadata have overlooked some of the most important features of signs. A sign has three aspects: it is (1) an entity that represents (2) another entity to (3) an agent. By looking only at the signs themselves, some metadata proposals have lost sight of the entities they represent and the agents – human, animal, or robot – which interpret them. With its three branches of syntax, semantics, and pragmatics, semiotics provides guidelines for organizing and using signs to represent something to someone for some purpose. Besides representation, semiotics also supports methods for translating patterns of signs intended for one purpose to other patterns intended for different but related purposes. This article shows how the fundamental semiotic primitives are represented in semantically equivalent notations for logic, including controlled natural languages and various computer languages.

/pdf/ontology-metadata-and-semiotics-43u3ns41xh.pdf

Ontology, Metadata, and Semiotics

Dealing with the new user cold-start problem in recommender systems: A comparative review

Semantic technologies are of paramount importance to the future Internet. The reuse and integration of semantically described resources, such as data or services, necessitates the bringing of ontologies into mutual agreement. Ontology alignment deals with the discovery of correspondences between concepts and relations from different ontologies. Alignment provides the key ingredient to semantic interoperability. This paper gives an overview on the state of the art in the field of visually supported semi-automatic alignment techniques and presents recent trends and developments. Particular attention is given to user interfaces and visualization techniques supporting involvement of humans in the alignment process. We derive and summarize requirements for visual semi-automatic alignment systems, provide an overview of existing approaches, and discuss the possibilities for further improvements and future research.

Ontology Alignment—A Survey with Focus on Visually Supported Semi-Automatic Techniques

AI foundations.- applications of AI.- cognition and intelligent interactions.-computer-aided education.- constraint and search.- creativity support.-decision theory.- evolutionary computation.- game playing.- information retrieval and extraction.- knowledge mining and acquisition.- knowledge representation and logic.- linked open data and semantic web.- machine learning and data mining.- multimedia and AI.- natural language processing.- robotics.- social intelligence.- vision and perception.- web and text mining.- web and knowledge-based system.

PRICAI 2012 : trends in artificial intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia, September 3-7, 2012 : proceedings

Case-based reasoning (CBR) has been widely used in many real-world applications. In general, CBR systems propose their answers based on solutions attached with the most similar cases retrieved from their case bases. However, in our vehicle insurance domain where the dataset contains a large amount of inconsistencies, proposing solutions based only on the most similar cases results in unacceptable answers. In this article, we propose a hybrid-reasoning algorithm which employs a number of statistical models derived from analysis of the entire dataset as an alternative reasoning method. Results of our experiments have shown that the use of these models enable our experimental system to propose better solutions than answers proposed based only on the closest matched cases.

Using statistical models and case-based reasoning in claims prediction: experience from a real-world problem

Efficiently indexing and retrieving cases from a very large case library are major concerns when building a Case-Based Reasoning (CBR) system. Most CBR research has focused on representation of cases, how to identify features that should be used for retrieval; and similarity measurement between values of attributes. In this paper, we propose a method for dynamically creating indices, and, also different similarity-measurement methods for different types of attributes. We also discuss the use of a relational database for representing cases, taxonomy knowledge, and spatial information. Our real world problem domain consists of 2 million incomplete insurance cases, with 30 different attributes. Even though all of these are valid cases, only 10 percent of these policies have lodged claims. These situations create a very complex case base for reasoning and problem solving. In response to this complexity, the approach adopted in building our CBR system involves a considerable amount of statistical pre-analysis of the contents of the case base to generate domain knowledge that could be used by the “Dynamic Index Creation Mechanism”. The main contribution of this paper is in describing the techniques used in our CBR system to dynamically create indices for the purpose of effective case retrieval.

Dynamically Creating Indices for Two Million Cases: A Real World Problem

Agent architecture has been one of the core components in building an agent application. Agent architecture is considered as the functional brain of an agent in making decision and reasoning to solve problem and achieving goals. This paper reviews some of the existing agent architectures such as logic-based architecture, reactive architecture, BDI architecture, hybrid architecture, cognitive architecture, and semantic architecture. The purpose of this study is to identify distinctive features of the different types of agent architectures and how they are implemented to solve real world problems.

/pdf/agent-architecture-an-overviews-1yvf15wpln.pdf

Agent Architecture: An Overviews

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