Social Mental Shaping: Modelling the Impact of Sociality on Autonomous Agents' Mental States

This paper presents a framework that captures how the social nature of agents that are situated in a multi-agent environment impacts upon their individual mental states. Roles and relationships provide an abstraction upon which we develop the notion of social mental shaping. This allows us to extend the standard Belief-Desire-Intention model to account for how common social phenomena (e.g. cooperation, collaborative problem-solving and negotiation) can be integrated into a unified theoretical perspective that reflects a fully explicated model of the autonomous agent's mental state

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved \ud version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed

Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support

A framework and methodology---termed LogiKEy---for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy's unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain theories in expressive classic higher-order logic (HOL). This meta-logical approach enables the provision of powerful tool support in LogiKEy: off-the-shelf theorem provers and model finders for HOL are assisting the LogiKEy designer of ethical intelligent agents to flexibly experiment with underlying logics and their combinations, with ethico-legal domain theories, and with concrete examples---all at the same time. Continuous improvements of these off-the-shelf provers, without further ado, leverage the reasoning performance in LogiKEy. Case studies, in which the LogiKEy framework and methodology has been applied and tested, give evidence that HOL's undecidability often does not hinder efficient experimentation.Comment: 50 pages; 10 figure

Extending the Carrel system to mediate in the organ and tissue allocation processes: a first approach

In this paper we extend the formalization of Carrel, a virtual organization for the procurement of tissues for transplantation purposes, in order to model also the procurement of human organs for transplants. We will focus in the organ allocation process to show how it can be formalized with the ISLANDER formalism. Also we present a first mechanism to federate the institution in several geographically-distributed platforms.Postprint (published version

Regulated MAS: Social Perspective

This chapter addresses the problem of building normative multi-agent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multi-agent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these mattersMunindar Singh’s effort was partially supported by the U.S. Army Research Office under grant W911NF-08-1-0105. The content of this paper does not necessarily reflect the position or policy of the U.S. Government; no official endorsement should be inferred or implied. Nicoletta Fornara’s effort is supported by the Hasler Foundation project nr. 11115-KG and by the SER project nr. C08.0114 within the COST Action IC0801 Agreement Technologies. Henrique Lopes Cardoso’s effort is supported by Fundação para a Ciência e a Tecnologia (FCT), under project PTDC/EIA-EIA/104420/2008. Pablo Noriega’s effort has been partially supported by the Spanish Ministry of Science and Technology through the Agreement Technologies CONSOLIDER project under contract CSD2007-0022, and the Generalitat of Catalunya grant 2009-SGR-1434.Peer Reviewe

Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex