Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009).Acknowledgments. The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009)

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Decision support systems are of many kinds depending on the models and techniques employed in them. Multiple criteria decision making techniques constitute an important class of DSS with unique software requirements. This paper stresses the importance of interactive MCDM methods since these facilitate learning through all stages of the decision making process. We first describe some features of Multiple Criteria Decision Support Systems ( MCDSSs) that distinguish them from classical DSSs. We then outline a software architecture for a MCDSS which has three basic components: a Dialog Manager, an MCDM Model Manager, and a Data Manager. We describe the interactions that occur between these three software components in an integrated MCDSS and outline a design for the Data Manager which is based on a concept of levels of data abstraction.Information Systems Working Papers Serie

Introduction to Decision Support Systems

Decision support systems (DSSs) are computer programs that, by using expert knowledge, simulation models and/or databases, are of assistance in the decision-making process as they offer management recommendations and/or options. The principal aim of a DSS is to improve the quality, speed and effectiveness of decisions. Since their beginnings in the 1960s, DSSs have been established as being an effective decision-making tool in different areas including agriculture. Weed science has not been immune to their influence, and since the end of the 1980s, a batch of DSSs have been developed towards the recognition and identification of seeds and seedlings, herbicide selection and the economic assessment of management strategies. Despite being powerful tools, DSSs have certain constraints and also a given resistance to their use. I hope that this chapter will serve to give a general insight into DSSs and their use in weed science, as well as to encourage the spreading of these systems in order to establish sustainable agriculture