A Hybrid Artificial Reputation Model

Agent interaction in a community such as an online buyer-seller scenario is often risky and uncertain. An agent interacts with other agents where initially they know nothing about each other. Currently many reputation models are developed that help consumers select more reputable and reliable service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information, or they are built upon witness information, also known as word-of-mouth, that involves the reports provided by others. Neither the interaction trust nor the witness information models alone fully succeed in such uncertain interactions. This thesis research introduces the hybrid reputation model combining both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputed service providers, eventually lead to more gains by the consumer. Furthermore, the trust model developed is used in calculating trust values of service providers for the case study with a live website ecommerce

Agent Modeling In Decision Support System: A Case Study In A Base Hospital System

Health practitioners are studying different techniques to provide quality patient care and to prevent injuries in the hospitals, which motivate the ground work to model such a complex system with the objective to understand the chief social factors leading to injury. The underlying social factors such as socialization, task scheduling, domain knowledge and path finding contribute to the day-to-day activity of the health practitioners and agents in social models which ultimately affect their performance. The aim of this study is to outline the objective decision support elements in mission critical human social models and critically examine the influence of those factors on the system. The outcome of this research leads to a development of more realistic artificial agents in a social complex modeling for the better understanding of the system\u27s behavior

Robust and Scalable Coordination of Potential-Field Driven Agents

Contains fulltext : 56522.pdf (publisher's version ) (Open Access)In this paper, we introduce a nature-inspired multiagent system for the task domain of resource distribution in large storage facilities. The system is based on potential fields and swarm intelligence, in which straightforward path planning is integrated. We show both experimentally and theoretically that the system is adaptive, robust and scalable. Moreover, we show that the planning component helps to overcome common pitfalls for nature-inspired systems in the task assignment domain. We end this paper with a discussion of an additional requirement for multi-agent systems interacting with humans: functionality. More precisely, we argue that such systems must behave in a fair way to be functional. We illustrate how fairness can be measured and illustrate that our system behaves in a moderately fair manner

Robust and Scalable Coordination of Potential-Field Driven Agents

In this paper, we introduce a nature-inspired multi-agent system for resource distribution in large storage facilities. The system is based on potential fields and swarm intelligence, in which straightforward path planning is integrated. We show both experimentally and theoretically that the system is adaptive, robust and scalable. Moreover, we show that the planning component helps to overcome common pitfalls for nature-inspired systems in the task assignment domain. We end this paper with a discussion of an additional requirement for multi-agent systems interacting with humans: functionality. More precisely, we argue that such systems must behave in a fair way to be functional. We illustrate how fairness can be measured and illustrate that our system behaves in a moderately fair manner.