Organisational Intelligence and Distributed AI

The analysis of this chapter starts from organisational theory, and from this it draws conclusions for the design, and possible organisational applications, of Distributed AI systems. We first review how the concept of organisations has emerged from non-organised "blackbox" entities to so-called "computerised" organisations. Within this context organisational researchers have started to redesign their models of intelligent organisations with respect to the availability of advanced computing technology. The recently emerged concept of Organisational Intelligence integrates these efforts in that it suggests five components of intelligent organisational skills (communication, memory, learning, cognition, problem solving). The approach integrates human and computer-based information processing and problem solving capabilities.<br/

Analyzing the Effects of Role Configuration in Logistics Processes using Multiagent-Based Simulation: An Interdisciplinary Approach

Current trends like the digital transformation and Industry 4.0 are challenging logistics management: flexible process development and optimization has been a primary concern in research in the last two decades. However, flexibility is limited by its underlying distribution of action and task knowledge. Thus, our objective is to develop an approach to optimize performance of logistics processes by dynamic (re-) configuration of knowledge in teams. One of the key assumptions for that approach is, that the distribution of knowledge has impact on team‘s performance. Consequently, we propose a formal specification for representing active resources (humans or smart machines) and distribution of action knowledge in multiagent-based simulation. In the second part of this paper, we analyze process quality in a psychologically validated laboratory case study. Our simulation results support our assumption, i.e., the results show that there is significant influence of knowledge distribution on process quality

Normative Emotional Agents: a viewpoint paper

[EN] Human social relationships imply conforming to the norms, behaviors and cultural values of the society, but also socialization of emotions, to learn how to interpret and show them. In multiagent systems, much progress has been made in the analysis and interpretation of both emotions and norms. Nonetheless, the relationship between emotions and norms has hardly been considered and most normative agents do not consider emotions, or vice-versa. In this article, we provide an overview of relevant aspects within the area of normative agents and emotional agents. First we focus on the concept of norm, the different types of norms, its life cycle and a review of multiagent normative systems. Secondly, we present the most relevant theories of emotions, the life cycle of an agent¿s emotions, and how emotions have been included through computational models in multiagent systems. Next, we present an analysis of proposals that integrate emotions and norms in multiagent systems. From this analysis, four relationships are detected between norms and emotions, which we analyze in detail and discuss how these relationships have been tackled in the reviewed proposals. Finally, we present a proposal for an abstract architecture of a Normative Emotional Agent that covers these four norm-emotion relationships.This work was supported by the Spanish Government project TIN2017-89156- R, the Generalitat Valenciana project PROMETEO/2018/002 and the Spanish Goverment PhD Grant PRE2018-084940.Argente, E.; Del Val, E.; Pérez-García, D.; Botti Navarro, VJ. (2022). Normative Emotional Agents: a viewpoint paper. IEEE Transactions on Affective Computing. 13(3):1254-1273. https://doi.org/10.1109/TAFFC.2020.3028512S1254127313

Multi-item Auctions for Automatic Negotiation

Dans un environnement multiagent, les ressources peuvent toujours s'avérer insuffisantes relativement à un nombre élevé de demandes. Dans ce cahier, nous proposons une approche mixant les enchères et les agents logiciels en vue de contribuer à résoudre ce problème. Cette approche consiste en fait à utiliser le mécanisme d'enchères multi-articles en vue d'allouer les ressources à un ensemble d'agents. À cet effet, nous considérons le problème de ressources comme un marché dans lequel évoluent des agents acheteurs et des agents vendeurs négociant des articles représentant des ressources. Ces agents utilisent des enchères multi-articles et par conséquent ils constituent un processus de négociation automatisé et programmé comme un réseau d'agents logiciels. Dans ce type de négociation, chaque agent exhibe différentes capacités d'acquisition lui permettant ainsi d'agir différemment selon le contexte ou la situation de marché. Par exemple, plus on est riche, plus on peut acheter d'articles. Nous présentons pour ce modèle une enchère anglaise et nous discuterons ses résultats expérimentaux.Available resources can often be limited with regard to the number of demands. In this paper we propose an approach for solving this problem which consists of using the mechanisms of multi-item auctions for allocating the resources to a set of software agents. We consider the resource problem as a market in which there are vendor agents and buyer agents trading on items representing the resources. These agents use multi-item auctions which are viewed here as a process of automatic negotiation, and implemented as a network of intelligent software agents. In this negotiation, agents exhibit different acquisition capabilities which let them act differently depending on the current context or situation of the market. For example, the "richer" an agent is, the more items it can buy, i.e. the more resources it can acquire. We present a model for this approach based on the English auction, then we discuss experimental evidence of such a model

Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area

Exploiting simple corporate memory in iterative coalition games

Amongst the challenging problems that must be addressed in order to create increasingly automated electronic commerce systems are those which involve forming coalitions of agents to exploit a particular market opportunity. Furthermore economic systems are normally continuous dynamic systems generating many instances of the same or similar problems (the regular calls for tender, regular emergence of new markets etc.).The work described in this paper explores how simple forms of memory can be exploited by agents over time to guide decision making in iterative sequences of coalition formation problems enabling them to build up social knowledge in order to improve their own utility and the ability of the population to produce increasingly well suited coalitions for a simple call-for-tender economy.Postprint (published version

Paving the way for Large-Scale Combinatorial Auctions

The Winner Determination Problem (WDP) in Combinatorial Auctions comes up in a wide range of applications. Linear Programming (LP) relaxations are a standard method for approximating combinatorial optimisation problems. In this paper we propose how to encode the WDP so that it can be approximated with AD3. Moreover, we contribute with PAR-AD3, the first parallel implementation of AD3. We show that while AD3 is up to 4.6 times faster than CPLEX in a single-thread execution, PAR-AD3 is up to 23 times faster than parallel CPLEX in an 8-core architecture. Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.This research has been supported by MICINN-Spain under contracts TIN2011-28689-C02-01, TIN2013-45732-C4-4-P and TIN2012-38876-C02-01.Peer reviewe

Detection and resolution of normative conflicts in multi-agent systems : a literature survey

Peer reviewedPostprin