Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment

Software Agents for Automated Transaction Negotiations: Implementation and Evaluation

Software agents have the potential to serve as effective surrogates of humans in conducting business transactions in an electronic commerce environment. The reported proceeding research aims to evaluate the performance of software agents in automated transaction negotiations. As part of this research, agents are being built using IBM aglets, and their performance evaluation within various experimental settings is currently underway

Three Decision-making Mechanisms to facilitate Negotiation of Service Level Agreements for Web Service Compositions

The negotiation of Service Level Agreements for composite web services is a very complex process. It involves the coordination of the negotiation process so that the end-to-end QoS requirements of the user request are satisfied while ensuring that the atomic QoS requirements are also simultaneously satisfied. This paper summarizes three decision-making mechanisms which support the process of Service Level Agreement negotiation for composite web services. The mechanisms include: the decomposition of the overall user preferences into the preferences of individual negotiation agents representing each atomic services within the composition; the selection of the prospective negotiation partners for the actual interaction from a list of potential service providers and finally the negotiation of Service Level Agreement with the selected provider agents while ensuring that the end-to-end QoS is satisfied

SLA Automated Negotiation Manager for Computing Services

Success in today’s marketing arena can often depend on companies embracing effective new technologies and integrating them into their business model. In the computing service supply industry, Service Level Agreements (SLAs) are commonly prepared and signed agreements between the service provider and its customers. SLAs should match business needs of both sides of the agreement as closely as possible. This paper focuses on at the steps and activities that the service provider can take to facilitate agreement. It proposes an automated way for creating SLA’s from a set of Service Level Objectives (SLOs). The SLA should achieve business goals, including the maximization of customer satisfaction. To automate the preparation of effective SLAs each company should set SLOs that support business needs

Learning in Multi-Agent Information Systems - A Survey from IS Perspective

Multiagent systems (MAS), long studied in artificial intelligence, have recently become popular in mainstream IS research. This resurgence in MAS research can be attributed to two phenomena: the spread of concurrent and distributed computing with the advent of the web; and a deeper integration of computing into organizations and the lives of people, which has led to increasing collaborations among large collections of interacting people and large groups of interacting machines. However, it is next to impossible to correctly and completely specify these systems a priori, especially in complex environments. The only feasible way of coping with this problem is to endow the agents with learning, i.e., an ability to improve their individual and/or system performance with time. Learning in MAS has therefore become one of the important areas of research within MAS. In this paper we present a survey of important contributions made by IS researchers to the field of learning in MAS, and present directions for future research in this area

Predicting the expected behavior of agents that learn about agents: the CLRI framework

We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relies on parameters which capture the agent's learning abilities, such as its change rate, learning rate and retention rate, as well as relevant aspects of the MAS such as the impact that agents have on each other. We validate the framework with experimental results using reinforcement learning agents in a market system, as well as with other experimental results gathered from the AI literature. Finally, we use PAC-theory to show how to calculate bounds on the values of the learning parameters

Employing Intelligent Agents to Automate SLA Creation

Service Level Agreements (SLAs) are commonly prepared and signed agreements that form the contracts between a service provider and its customers, defining the obligations and liabilities of the parties. Naturally, SLAs should reflect the business needs of both customer and supplier. SLAs are usually formed through either the adoption of a boilerplate agreement from the provider, or through a mediation/negotiation process between the parties. With the increasing adoption of software supply being implemented as a network service, such schemes are rigid or slow and costly, This paper proposes a system that the parties can use to facilitate both fast and flexible agreements. It proposes automation of SLA creation from a set of Service Level Objectives (SLOs), making use of software agents and adopting a social order function by incorporating it into the decision process

Coordination of Supply Webs Based on Dispositive Protocols

A lot of curricula in information systems, also at master level, exists today. However, the strong need in new approaches and new curricula still exists, especially, in European area. The paper discusses the modern curriculum in information systems at master level that is currently under development in the Socrates/Erasmus project MOCURIS. The curriculum is oriented to the students of engineering schools of technical universities. The proposed approach takes into account integration trends in European area as well as the transformation of industrial economics into knowledge-based digital economics The paper presents main characteristics of the proposed curriculum, discuses curriculum development techniques used in the project MOCURIS, describes the architecture of the proposed curriculum and the body of knowledge provided by it