1,720,967 research outputs found
A Heuristic Approach to Proposal-Based Negotiation with Applications in Fashion Supply Chain Management
No full textWe reconsider and improve the formal, executable framework for automated multi-issue negotiation between two autonomous competitive software agents proposed by Cadoli, who introduced a proposal-based process of negotiation, which he modeled as a distributed constraint satisfaction problem (DCSP). His model is based on the view of negotiation spaces, representing the admissible values of the goods involved in the process, as convex regions: i.e., all points included in each agent’s individual region (or “area”) correspond to acceptable agreements. However, in order to speed-up the negotiation process and guarantee convergence, there was the assumption/limitation that, for one of the two agents, only vertices of individual negotiation space were considered as admissible offers. Therefore, only those vertices included in the intersection of the two areas where actual potential agreements. In this article, we present and assess experimentally an extension to Cadoli’s approach where, in particular, interaction is no longer vertex-based, or at least not necessarily. I.e., we allow both agents to potentially make offers that are an internal point of its negotiation space and then try to approach the opponent’s counter-proposal “step-by-step”. The algorithm presented here overcomes some problems of the original one, such as the asymmetry among the parties and the limitation to polyhedral negotiation areas. Also, the extension can be usefully integrated to Cadoli’s framework, thus obtaining a new algorithm that may be effective in many practical cases by introducing “local search,”, for instance around
“best-preferred” vertices. We present and discuss a number of experiments, aimed at assessing how parameters influence the performance of the algorithm, and they relate to each other. We report that the extended approach works properly, and in several cases yields a better solution than the original proposal, while the interaction complexity remains acceptable. We discuss its applicability in relevant application fields, such as for instance supply chain management in the fashion industry, which is a field of growing importance in economy and e-commerce
DALICA Agents applied to a Cultural Heritage scenario
No full textIn this paper we discuss the potential contributions that agent technology can
bring into an Ambient Intelligence scenario, related to the fruition and monitoring
of cultural assets. The users are located in an area which is known to the agents:
in the application, for the cultural assets scenario the users are the visitors of Villa
Adriana, an archaeological site in Tivoli, near Rome (Italy) while for the monitoring
scenario works of arts have been transported from a museum in Rome to
another one in Florence. Agents are aware of user moves by means of Galileo
satellite signal, i.e., the proposed application is based on a blend of different technologies.
The agents, developed in the DALI logic programming language, proactively
learn and/or enhance users profiles and are thus capable to competently
assist the users during their visit, to elicit habits and preferences and to propose
cultural assets to the users according to the learned profile
User profile agents for cultural heritage fruition
No full textIn this paper we present an application of a MAS (Multi-Agent System) composed of logical agents in an Ambient Intelligence scenario, related to the fruition of cultural assets. The users are located in an area which is known to the agents: in the application, the users are the visitors of Villa Adriana, an archaeological site in Tivoli, near Rome (Italy). Agents are aware of user moves by means of Galileo satellite signal, i.e., the proposed application is based on a blend of different technologies. The agents, developed in the DALI logic programming language, proactively learn and/or enhance users profiles and are thus capable to competently assist the users during their visit, to elicit habits and preferences and to propose cultural assets to the users according to the learned profile
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