Personal Agents for Implicit Culture Support

We present an implementation of a multi-agent system that aims at solving the problem of tacit knowledge transfer by means of experiences sharing. In particular, we consider experiences of use of pieces of information. Each agent incorporates a system for implicit culture support (SICS) whose goal is to realize the acceptance of the suggested information. The SICS permits a transparent (implicit) sharing of the information about the use, e.g., requesting and accepting pieces of information

Social learning in a multi-agent system

In a persistent multi-agent system, it should be possible for new agents to benefit from the accumulated learning of more experienced agents. Parallel reasoning can be applied to the case of newborn animals, and thus the biological literature on social learning may aid in the construction of effective multi-agent systems. Biologists have looked at both the functions of social learning and the mechanisms that enable it. Many researchers have focused on the cognitively complex mechanism of imitation; we will also consider a range of simpler mechanisms that could more easily be implemented in robotic or software agents. Research in artificial life shows that complex global phenomena can arise from simple local rules. Similarly, complex information sharing at the system level may result from quite simple individual learning rules. We demonstrate in simulation that simple mechanisms can outperform imitation in a multi-agent system, and that the effectiveness of any social learning strategy will depend on the agents' environment. Our simple mechanisms have obvious advantages in terms of robustness and design costs

A Multi-Agent Approach Towards Collaborative Supply Chain Management

Supply chain collaboration has become a critical success factor for supply chain management and effectively improves the performance of organizations in various industries. Supply chain collaboration builds on information sharing, collaborative planning and execution. Information technology is an important enabler of collaborative supply chain management. Many information systems have been developed for supply chain management from legacy systems and enterprise resource planning (ERP) into the newly developed advanced planning and scheduling system (APS) and e-commerce solutions. However, these systems do not provide sufficient support to achieve collaborative supply chain. Recently, intelligent agent technology and multi-agent system (MAS) have received a great potential in supporting transparency in information flows of business networks and modeling of the dynamic supply chain for collaborative supply chain planning and execution. This paper explores the similarities between multi-agent system and supply chain system to justify the use of multi-agent technology as an appropriate approach to support supply chain collaboration. In addition, the framework of the multi-agent-based collaborative supply chain management system will be presented

Cloud-Based Centralized/Decentralized Multi-Agent Optimization with Communication Delays

We present and analyze a computational hybrid architecture for performing multi-agent optimization. The optimization problems under consideration have convex objective and constraint functions with mild smoothness conditions imposed on them. For such problems, we provide a primal-dual algorithm implemented in the hybrid architecture, which consists of a decentralized network of agents into which centralized information is occasionally injected, and we establish its convergence properties. To accomplish this, a central cloud computer aggregates global information, carries out computations of the dual variables based on this information, and then distributes the updated dual variables to the agents. The agents update their (primal) state variables and also communicate among themselves with each agent sharing and receiving state information with some number of its neighbors. Throughout, communications with the cloud are not assumed to be synchronous or instantaneous, and communication delays are explicitly accounted for in the modeling and analysis of the system. Experimental results are presented to support the theoretical developments made.Comment: 8 pages, 4 figure

A DHT-Based Multi-Agent System for Semantic Information Sharing. In

Abstract. This paper presents AOIS, a multi-agent system that supports the sharing of information among a dynamic community of users connected through the Internet thanks to the use of a well-known DHT-based peer-to-peer platform: BitTorrent. In respect to Web search engines, this system enhances the search through domain ontologies, avoids the burden of publishing the information on the Web and guaranties a controlled and dynamic access to the information. The use of agent technologies has made the realization of three of the main features of the system straightforward: i) filtering of information coming from different users, on the basis of the previous experience of the local user, ii) pushing of some new information that can be of interest for a user, and iii) delegation of access capabilities, on the basis of a reputation network, built by the agents of the system on the community of its users. The use of BitTorrent will allow us to offer the AOIS systems to the hundreds of millions of users that already share documents though the BitTorrent platform

MASK-SM : multi-agent system based knowledge management system to support knowledge sharing of software maintenance knowledge environment.

Knowledge management (KM) has become an important topic as organizations wish to take advantage of the information that they produce and that can be brought to bear on present decisions. This paper described a system to manage the information and knowledge generated during the software maintenance process (SMP). Knowledge Management System (KMS) is utilizing to help employees build a shared vision, since the same codification is used and misunderstanding in staff communications may be avoided. The architecture of the system is formed from a set of agent communities each community of practice(CoP) is in charge of managing a specific type of knowledge. The agents can learn from previous experience and share their knowledge with other agents or communities in a group of multi-agent system (MAS). This paper also described on the theoretical concept and approach of multi-agent technology framework that could be implemented software maintenance process (SMP) in order to facilitate knowledge sharing among the maintainers of the learning organization. as well as to demonstrate it into the system wise, on how the multi-agent technology could be utilized in the software maintenance process (SMP) system model for serving the maintainer that is developed by using groupware such as Lotus Notes software. This architecture will be named as MASK-SM (MAS Architecture to Facilitate Knowledge Sharing of Software Maintenance). The author followed the Prometheus methodology to design the MAS architecture. This paper applied the definition of ISO 9241-11 (1998) that examines effectiveness, efficiency, and satisfaction. The emphasis will be given to the software maintenance process (SMP) activities that may concern with multi-agent technology to help the maintainers especially in learning organization to work collaboratively including critical success factor in order to ensure that software maintenance process (SMP) initiatives would be delivered competitive advantage for the community of practice(CoP) as well as users of the organization

Semantic Co-Browsing System Based on Contextual Synchronization on Peer-to-Peer Environment

In this paper, we focus on a personalized information retrieval system based on multi-agent platform. Especially, they are capable of sharing information between them, for supporting collaborations between people. Personalization module has to be exploited to be aware of the corresponding user's browsing contexts (e.g., purposes, intention, and goals) at the specific moment. We want to recommend as relevant information to the estimated user context as possible, by analyzing the interaction results (e.g., clickstreams or query results). Thereby, we propose a novel approach to self-organizing agent groups based on contextual synchronization. Synchronization is an important requirement for online collaborations among them. This synchronization method exploits contextual information extracted from a set of personal agents in the same group, for real-time information sharing. Through semantically tracking of the users' information searching behaviors, we model the temporal dynamics of personal and group context. More importantly, in a certain moment, the contextual outliers can be detected, so that the groups can be automatically organized again with the same context. The co-browsing system embedding our proposed method was shown 52.7 % and 11.5 % improvements of communication performance, compared to single browsing system and asynchronous collaborative browsing system, respectively