On the verge of a new relationship between man and artificial learning machines?

We are now living an exciting time in which astonishing results coming from the A.I. field trigger the usualexpectations and fears about intelligent man-machinerelationship. Some believe that the basic ingredientsfor the artificial general intelligence are already hereand, in their opinion, it is just a matter of putting it alltogether. It seems that we do not have to programcomputers anymore; they will program themselves.Should this statement frighten us?Our position is both of recognizing the big potentialities of new A.I. developments and, simultaneously,to warn against yet another overselling and possiblydamaging stage in the A.I. field.We advocate that, intelligent systems, relying inevolving machine learning algorithms, fully autonomoussoftware agents or robots, should always follow the"human in the loop" principle, ensuring that theresponsibility for all future intelligent entity activitiescan be traced back to some recognized and accountableindividuals or organizations

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations

Improving Assumption based Distributed Belief Revision

Belief revision is a critical issue in real world DAI applications. A Multi-Agent System not only has to cope with the intrinsic incompleteness and the constant change of the available knowledge (as in the case of its stand alone counterparts), but also has to deal with possible conflicts between the agents’ perspectives. Each semi-autonomous agent, designed as a combination of a problem solver – assumption based truth maintenance system (ATMS), was enriched with improved capabilities: a distributed context management facility allowing the user to dynamically focus on the more pertinent contexts, and a distributed belief revision algorithm with two levels of consistency. This work contributions include: (i) a concise representation of the shared external facts; (ii) a simple and innovative methodology to achieve distributed context management; and (iii) a reduced inter-agent data exchange format. The different levels of consistency adopted were based on the relevance of the data under consideration: higher relevance data (detected inconsistencies) was granted global consistency while less relevant data (system facts) was assigned local consistency. These abilities are fully supported by the ATMS standard functionalities

Distributed Belief Revision and Environmental Decision Support

This article discusses the development of an Intelligent Distributed Environmental Decision Support System, built upon the association of a Multi-agent Belief Revision System with a Geographical Information System (GIS). The inherent multidisciplinary features of the involved expertises in the field of environmental management, the need to define clear policies that allow the synthesis of divergent perspectives, its systematic application, and the reduction of the costs and time that result from this integration, are the main reasons that motivate the proposal of this project. This paper is organised in two parts: in the first part we present and discuss the developed - Distributed Belief Revision Test-bed - DiBeRT; in the second part we analyse its application to the environmental decision support domain, with special emphasis on the interface with a GIS

Ontology-services agent to help in the structural and semantic heterogeneity

In the Virtual Enterprises (VE) environment, interactions between distributed heterogeneous computing entities representing different enterprises, people and resources, take place. These interactions, in order to be both syntactic and semantic compatible, need to follow appropriate standards (ontologies) well understood by all the participants. Even for each domain ontology, people may store their data in different structures and use different terms to represent the same concept. This paper focuses on an effort to create an Ontology-Services Agent to monitor the communication acts taking place in a Multi-agent System. The Ontology-Services Agent provides help in solving the Structural and Semantic Heterogeneity problem, enabling appropriate conversations and making it possible meaningful agreements between agents representing different enterprises and resources in a VE environment

Monitoring cooperative business contracts in an institutional environment

The automation of B2B processes is currently a hot research topic. In particular, multi-agent systems have been used to address this arena, where agents can represent enterprises in an interaction environment, automating tasks such as contract negotiation and enactment. Contract monitoring tools are becoming more important as the level of automation of business relationships increase. When business is seen as a joint activity that aims at pursuing a common goal, the successful execution of the contract benefits all involved parties, and thus each of them should try to facilitate the compliance of their partners. Taking into account these concerns and inspecting international legislation over trade procedures, in this paper we present an approach to model contractual obligations: obligations are directed from bearers to counterparties and have flexible deadlines. We formalize the semantics of such obligations using temporal logic, and we provide rules that allow for monitoring them. The proposed implementation is based on a rule-based forward chaining production system

Flexible deadlines for directed obligations in agent-based business contracts

In B2B contract enactment, cooperation should be takeninto account when modeling contractual commitments throughobligations. We advocate a directed deadline obligation approach,taking inspiration on international legislation overtrade procedures. Our proposal is based on authorizationsgranted in specific states of an obligation lifecycle model.Flexible deadlines provide an additional level of cooperationbetween contractual agents. Moreover, agents increase theirdecision-making options concerning obligations

Adaptive deterrence sanctions in a normative framework

Normative environments are used to regulate multi-agent interactions. In business encounters, agents representing business entities make contracts including norms that prescribe what agents should do. Agent autonomy, however, gives agents the ability to decide whether they fulfill or violate their commitments. In this paper we present an adaptive mechanism that enables a normative framework to change deterrence sanctions according to an agent population, in order to preclude agents from exploiting potential normative flaws. The system tries to avoid institutional control beyond what is strictly necessary, seeking to maximize agent contracting activity while ensuring a certain commitment compliance level, when agents have unknown risk and social attitudes

Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects

This paper describes a preliminary study for producing and distributing a large-scale database of embeddings from the Portuguese Twitter stream. We start by experimenting with a relatively small sample and focusing on three challenges: volume of training data, vocabulary size and intrinsic evaluation metrics. Using a single GPU, we were able to scale up vocabulary size from 2048 words embedded and 500K training examples to 32768 words over 10M training examples while keeping a stable validation loss and approximately linear trend on training time per epoch. We also observed that using less than 50\% of the available training examples for each vocabulary size might result in overfitting. Results on intrinsic evaluation show promising performance for a vocabulary size of 32768 words. Nevertheless, intrinsic evaluation metrics suffer from over-sensitivity to their corresponding cosine similarity thresholds, indicating that a wider range of metrics need to be developed to track progress