Weaving a fabric of socially aware agents

The expansion of web-enabled social interaction has shed light on social aspects of intelligence that have not been typically studied within the AI paradigm so far. In this context, our aim is to understand what constitutes intelligent social behaviour and to build computational systems that support it. We argue that social intelligence involves socially aware, autonomous individuals that agree on how to accomplish a common endeavour, and then enact such agreements. In particular, we provide a framework with the essential elements for such agreements to be achieved and executed by individuals that meet in an open environment. Such framework sets the foundations to build a computational infrastructure that enables socially aware autonomy.This work has been supported by the projects EVE(TIN2009-14702-C02-01) and AT (CSD2007-0022)Peer Reviewe

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

Online reverse auctions research in marketing versus SCM: A review and future directions

An online reverse auction (ORA) is a dynamic procurement mechanism that allows suppliers to compete in real time via a platform to gain a buyer’s business. The ORA is a technological tool introduced in the late 1990s, gaining proponents and detractors among practitioners and academics. Remarkably, while practitioner interestin ORAs has grown, related marketing and supply chain management (SCM) research has declined. This contradiction between theory and practice suggests the need to conduct a systematic review to provide readers with a state-of-the-art understanding of ORAs and recommend fruitful avenues for further research. We focus on the marketing literature and contrast the findings with SCM literature, in such an analysis practical relevance is stressed. Our study offers three main contributions: (1) integration of the cumulative marketing knowledge on ORAs in the 2002–2020 period, (2) development of a three-layer framework of the ORA domain (i.e., conceptualization, ORA as a process, and research setting), and (3) construction of a new research agenda to deal with scholarly challenges and emerging trends.Xunta de Galicia | Ref. GPC ED431B 2022/10Universidade de Vigo/CISU

Interaction and communication among autonomous agents in multiagent systems

The main goal of this doctoral thesis is to investigate a fundamental topic of research within the Multiagent Systems paradigm: the problem of defining open, heterogeneous, and dynamic interaction frameworks. That is to realize interaction systems where multiple agents can enter and leave dynamically and where no assumptions are made on the internal structure of the interacting agents. Such topic of research has received much attention in the past few years. In particular the need to realize applications where artificial agents can interact negotiate, exchange information, resources, and services has become more and more important thanks to the advent of Internet. I started my studies by developing a trading agent that took part to an international trading on-line game: the First Trading Agent Competition (TAC). During the design and development phase of the trading agent some crucial and critical troubles emerged: the problem of accurately understanding the rules that govern the different auctions; and the problem of understanding the meaning of the numerous messages. Another general problem is that the internal structure of the developed trading agent have been strongly determined by the peculiar interface of the interaction system, consequently without any changes in its code, it would not be able to take part to any other competition on the Web. Furthermore the trading agent would not have been able to exploit opportunities, to handle unexpected situations, or to reason about the rules of the various auctions, since it is not able to understand the meaning o the exchanged messages. The presence of all those problems bears out the need to find a standard common accepted way to define open interaction systems. The most important component of every interaction framework, as is remarked also by philosophical studies on human communication is the institution of language. Therefore I start to investigate the problem of defining a standard and common accepted semantics for Agent Communication Languages (ACL). The solutions proposed so far are at best partial, and are considered as unsatisfactory by a large number of specialists. In particular, they are unable to support verifiable compliance to standards and to make agents responsible for their communicative actions. Furthermore such proposals make the strong assumption that every interacting agent may be modeled as a Belief-Desire-Intention agent. What is required is an approach focused on externally observable events as opposed to the unobservable internal states of agents. Following Speech Act Theory that views language use as a form of action, I propose an operational specification for the definition of a standard ACL based on the notion of social commitment. In such a proposal the meaning of basic communicative acts is defined as the effect that it has on the social relationship between the sender and the receiver described through operation on an unambiguous, objective, and public "object": the commitment. The adoption of the notion of commitment is crucial to stabilize the interaction among agents, to create an expectation on other agents behavior, to enable agents to reason about their and other agents actions. The proposed ACL is verifiable, that is, it is possible to determine if an agent is behaving in accordance to its communicative actions; the semantics is objective, independent of the agent's internal structure, flexible and extensible, simple, yet enough expressive. A complete operational specification of an interaction framework using the proposed commitment-based ACL is presented. In particular some sample applications of how to use the proposed framework to formalize interaction protocols are reported. A list of soundness conditions to test if a protocol is sound is proposed