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and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated.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 growing use of Information Technology in the commercial arena leads to an urgent need to find alternatives to traditional dispute resolution. New tools from fields such as Artificial Intelligence should be considered in the process of developing novel Online Dispute Resolution platforms, in order to make the ligation process simpler, faster and conform with the new virtual environments. In this work, we describe UMCourt, a project built around two sub-fields of Artificial Intelligence research: Multi-agent Systems and Case-based Reasoning, aimed at fostering the development of tools for Online Dispute Resolution. This is then used to accomplish several objectives, from suggesting solutions to new disputes based on the observation of past similar disputes, to the improvement of the negotiation and mediation processes that may follow. The main objective of this work is to develop autonomous tools that can increase the effectiveness of the dispute resolution processes, namely by increasing the amount of meaningful information that is available for the parties.

Abstr act. When contracting through software agents, disputes will inevitably arise. Thus there is an urgent need to find alternatives to litigation for resolving conflicts. Methods of Online Dispute Resolution (ODR) need to be considered to resolve such disputes. Having agents understanding what the dispute is about, managing all interaction between the parties and even formulating proposed solutions is an important innovation. Hence it is of the utmost relevance that the agents may be able to recognise and evaluate the facts, the position of the parties and understand all the relevant data. In many circumstances, risk management and avoidance will be a crucial point to be considered. In this sense we analyze the usefulness of a parallel concept to BATNA-Best Alternative to Negotiated Agreement, that of a WATNA-Worst Alternative to Negotiated Agreement, allowing the software agents to consider the space between BATNA and WATNA as a useful element to be taken into account when making or accepting a proposal. These software agents embodied with intelligent techniques are integrated in an architecture designed to provide support to the ODR in a system we have developed for the resolution of labour disputes-UMCourt. In this context software agents are used to compute and provide the parties with the best and worst alternative to a negotiated agreement.

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.

No abstract