Security framework for mobile agents-based applications

Mobile agents have been proposed for key applications such as forensics analysis, intrusion detection, e-commerce, and resource management. Yet, they are vulnerable to various security threats by malicious hosts or intruders. Conversely, genuine platforms may run malicious agents. It is essential to establish a truly secure framework for mobile agents to gain trust of clients in the system. Failure to accomplish a trustworthy secured framework for Mobile Agent System (MAS) will limit their deployment into the key applications. This chapter presents a comprehensive taxonomy of various security threats to Mobile Agent System and the existing implemented security mechanisms. Different mechanisms are discussed, and the related security deficiencies are highlighted. The various security properties of the agent and the agent platform are described. The chapter also introduces the properties, advantages, and roles of agents in various applications. It describes the infrastructure of the system and discusses several mobile agent frameworks and the accomplished security level.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

Negotiating agents that learn about others’ preferences

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents’ preferences via past interactions. Over time, the agents can incrementally update their models of other agents’ preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situation where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

A framework for co-ordination and learning among teams of agents

We present a framework for team coordination under incomplete information based on the theory of incomplete information games. When the true distribution of the uncertainty involved is not known in advance, we consider a repeated interaction scenario and show that the agents can learn to estimate this distribution and share their estimations with one another. Over time, as the set of agents' estimations become more accurate, the utility they can achieve approaches the optimal utility when the true distribution is known, while the communication requirement for exchanging the estimations among the agents can be kept to a minimal level.

Agents in adversarial domains - modelling environments in parallel

We present a model of an environment to evaluate the behavior of an agent trying to hide from a pursuer is presented. The model computes the direction and the amount of protection provided by the environment. The computational complexity of this problem is improved by using a parallel implementation of this algorithm.

Investigational agents that protect pancreatic islet β-cells from failure

Type 2 diabetes is associated with insulin resistance and reduced insulin secretion, which results in hyperglycaemia. This can then lead to diabetic complications such as retinopathy, neuropathy, nephropathy and cardiovascular disease. Although insulin resistance may be present earlier in the progression of the disease, it is now generally accepted that it is the deterioration in insulin-secretory function that leads to hyperglycaemia. This reduction in insulin secretion in Type 2 diabetes is due to both islet β-cell dysfunction and death. Therefore, interventions that maintain the normal function and protect the pancreatic islet β-cells from death are crucial in the treatment of Type 2 diabetes so that plasma glucose levels may be maintained within the normal range. Recently, a number of compounds have been shown to protect β-cells from failure. This review examines the evidence that the existing therapies for Type 2 diabetes that were developed to lower plasma glucose (metformin) or improve insulin sensitivity (thiazolidinediones) may also have islet-protective function. Newer emerging therapeutic agents that are designed to increase the levels of glucagon-like peptide-1 not only stimulate insulin secretion but also appear to increase islet β-cell mass. Evidence will also be presented that the future of drug therapy designed to prevent β-cell failure should target the formation of advanced glycation end products and alleviate oxidative and endoplasmic reticulum stress.

Nurse-led titration of angiotensin converting enzyme inhibitors, beta-adrenergic blocking agents and angiotensin receptor blockers for patients with left ventricular systolic dysfunction

This is the protocol for a review and there is no abstract. The objectives are as follows:

To assess the effects of nurse-led titration of ACEIs, beta-adrenergic blocking agents and ARBs in patients with left ventricular systolic dysfunction in terms of safety and patient outcomes.