Formal Interpretation of a Multi-Agent Society As a Single Agent

In this paper the question is addressed to what extent the collective processes in a multi-agent society can be interpreted as single agent processes. This question is answered by formal analysis and simulation. It is shown for an example process how it can be conceptualised, formalised and simulated in two different manners: from a single agent (or cognitive) and from a multi-agent (or social) perspective. Moreover, it is shown how an ontological mapping can be formally defined between the two formalisations, and how this mapping can be extended to a mapping of dynamic properties. Thus it is shown how collective behaviour can be interpreted in a formal manner as single agent behaviour.Collective Intelligence, Simulation, Logical Formalisation, Single Vs. Multi-Agent Behaviour

Formal Interpretation of a Multi-Agent Society as a Single Agent

Contains fulltext : 194702.pdf (publisher's version ) (Open Access)In this paper the question is addressed to what extent the collective processes in a multi-agent society can be interpreted as single agent processes. This question is answered by formal analysis and simulation. It is shown for an example process how it can be conceptualised, formalised and simulated in two different manners: from a single agent (or cognitive) and from a multi-agent (or social) perspective. Moreover, it is shown how an ontological mapping can be formally defined between the two formalisations, and how this mapping can be extended to a mapping of dynamic properties. Thus it is shown how collective behaviour can be interpreted in a formal manner as single agent behaviour.23 p

EDI and intelligent agents integration to manage food chains

Electronic Data Interchange (EDI) is a type of inter-organizational information system, which permits the automatic and structured communication of data between organizations. Although EDI is used for internal communication, its main application is in facilitating closer collaboration between organizational entities, e.g. suppliers, credit institutions, and transportation carriers. This study illustrates how agent technology can be used to solve real food supply chain inefficiencies and optimise the logistics network. For instance, we explain how agribusiness companies can use agent technology in association with EDI to collect data from retailers, group them into meaningful categories, and then perform different functions. As a result, the distribution chain can be managed more efficiently. Intelligent agents also make available timely data to inventory management resulting in reducing stocks and tied capital. Intelligent agents are adoptive to changes so they are valuable in a dynamic environment where new products or partners have entered into the supply chain. This flexibility gives agent technology a relative advantage which, for pioneer companies, can be a competitive advantage. The study concludes with recommendations and directions for further research

An Abstract Formal Basis for Digital Crowds

Crowdsourcing, together with its related approaches, has become very popular in recent years. All crowdsourcing processes involve the participation of a digital crowd, a large number of people that access a single Internet platform or shared service. In this paper we explore the possibility of applying formal methods, typically used for the verification of software and hardware systems, in analysing the behaviour of a digital crowd. More precisely, we provide a formal description language for specifying digital crowds. We represent digital crowds in which the agents do not directly communicate with each other. We further show how this specification can provide the basis for sophisticated formal methods, in particular formal verification.Comment: 32 pages, 4 figure