Pitfalls of Agent-Oriented Development

While the theoretical and experimental foundations of agent-based systems are becoming increasingly well understood, comparatively little effort has been devoted to understanding the pragmatics of (multi-) agent systems development - the everyday reality of carrying out an agent-based development project. As a result, agent system developers are needlessly repeating the same mistakes, with the result that, at best, resources are wasted - at worst, projects fail. This paper identifies the main pitfalls that await the agent system developer, and where possible, makes tentative recommendations for how these pitfalls can be avoided or rectified

Guide to the Networked Minds Social Presence Inventory v. 1.2

This document introduces the Networked\ud Minds Social Presence Inventory. The\ud inventory is a self-report measure of social\ud presence, which is commonly defined as the\ud sense of being together with another in a\ud mediated environment. The guidelines\ud provide background on the use of the social\ud presence scales in studies of users’ social\ud communication and interaction with other\ud humans or with artificially intelligent agents\ud in virtual environments

On Agent-Based Software Engineering

Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures

StochKit-FF: Efficient Systems Biology on Multicore Architectures

The stochastic modelling of biological systems is an informative, and in some cases, very adequate technique, which may however result in being more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and exploits the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data.Comment: 14 pages + cover pag

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

Joint fitting reveals hidden interactions in tumor growth

Tumor growth is often the result of the simultaneous development of two or more cancer cell populations. Their interaction between them characterizes the system evolution. To obtain information about these interactions we apply the recently developed vector universality (VUN) formalism to various instances of competition between tumor populations. The formalism allows us: (a) to quantify the growth mechanisms of a HeLa cell colony, describing the phenotype switching responsible for its fast expansion, (b) to reliably reconstruct the evolution of the necrotic and viable fractions in both in vitro and in vivo tumors using data for the time dependences of the total masses, and (c) to show how the shedding of cells leading to subspheroid formation is beneficial to both the spheroid and subspheroid populations, suggesting that shedding is a strong positive influence on cancer dissemination

Dynamics of interacting diseases

Current modeling of infectious diseases allows for the study of complex and realistic scenarios that go from the population to the individual level of description. However, most epidemic models assume that the spreading process takes place on a single level (be it a single population, a meta-population system or a network of contacts). In particular, interdependent contagion phenomena can only be addressed if we go beyond the scheme one pathogen-one network. In this paper, we propose a framework that allows describing the spreading dynamics of two concurrent diseases. Specifically, we characterize analytically the epidemic thresholds of the two diseases for different scenarios and also compute the temporal evolution characterizing the unfolding dynamics. Results show that there are regions of the parameter space in which the onset of a disease's outbreak is conditioned to the prevalence levels of the other disease. Moreover, we show, for the SIS scheme, that under certain circumstances, finite and not vanishing epidemic thresholds are found even at the thermodynamic limit for scale-free networks. For the SIR scenario, the phenomenology is richer and additional interdependencies show up. We also find that the secondary thresholds for the SIS and SIR models are different, which results directly from the interaction between both diseases. Our work thus solve an important problem and pave the way towards a more comprehensive description of the dynamics of interacting diseases.Comment: 24 pages, 9 figures, 4 tables, 3 appendices. Final version accepted for publication in Physical Review