Alicia Y.C. Tang

Agents of Things (AoT): An intelligent operational concept of the Internet of Things (IoT)

In this conceptual paper, we review the definitions, characteristics, and architecture of the Internet of Things (IoT) concept. We then identify the deficiencies of the IoT concept, analyze them, and discover the issue of lack of reasoning and intelligence in the IoT concept. We propose a solution to augment the IoT with intelligent software agents resulting in a new concept called the Agents of Things (AoT). The paper presents the AoT architecture and a scenario for one application of the proposed concept. Finally, it discusses the benefits of implementing AoT concept to solve real world issues and the future work.

Certainty, trust and evidence

We assume a group of agents, in which a process of opinion gathering takes place.We examine the values of agents reputation, trust and certainty.Confidence value for agents depend on the values of trust and certainty.Increasing the importance of trust increases the weight of trust in the confidences value calculations.Agents trust or agents certainty alone does not affect agents confidence value. Introducing confidence in multi-agent systems gives agents a form of control in making decisions and helps to improve the decision making process in such systems. Consequently, modeling confidence of agents is important in heterogeneous agent communities. The inability to detect an agents confidence can be a reason for inaccurate decision. Several weaknesses have been found in current trust and confidence models in multi-agent systems. Current models propose that the trust of an agent depends on its reputation, past experience, and observations on its behavior. This paper presents another approach to agent-based confidence modeling. Initially, it integrates two confidence requirements, namely, trust and certainty. To further strengthen the model, we include evidence as an additional requirement to the model by which trust and certainty of an agent can be verified. This paper establishes bisection between trust, certainty, and evidence spaces. The modeling mechanism eliminates untrusted opinions, since such certainty level might not be valuable in all states. The proposed technique also separates the global confidence scheme from the local confidence scheme, so as to provide greater reliability for confidence detection.

Norm's benefit awareness in open normative multi-agent communities: A conceptual framework

In open normative multi-agent communities, agents adopt new norms to increase their utilities. Several studies have developed mechanisms for agents to adopt new norms. These mechanisms are based on sanction, imitation, or social learning. The limitation of these mechanisms is that over time all agents follow the new norms, while in a real situation, usually there is a number of agents that persistently violate the norms for their benefits. We consider that intelligent agents should adopt new norms based on their awareness of the norms’ expected benefits on their utilities and not only by sanctions or imitating other agents. Consequently, this paper presents a conceptual framework for agents’ awareness of norms’ benefits in open normative multi-agent communities. In the proposed framework, four components constitute agents’ awareness of norms’ benefits which are Norm’s Adoption Ratio; Norm’s Yields; Norm’s Trust, and Norm’s Morality. Using these components, however, agents would be able to evaluate the benefits of detected norms and subsequently determine whether the norms increase or decrease their utilities for eventual adoption or rejection.

Modeling norms removal in open normative multi-agent system

Norms of negative consequences might be practiced in a society for a long time. These norms are mostly related to strong beliefs that prevent individuals from violating or abandoning them. In this case, these norms must be removed to reach a stable and benevolent society. Negative norms removal is presented in social science as a part of society evolution, yet it is not modeled in normative multi-agent systems. Norm removal in social science is done in two main stages. The first stage is called collective belief change. The second stage is called collective action. In this paper, we model these two stages into five processes which are, norm negativity realization, collective belief change, norm removal decision, choosing removal monitoring authority, and removal process. When the five stages are completed, agents make their own decision either to delete or not to delete the norm from their cognitive structure depending on their internal system status.

Norms Disappearance in Open Normative Multi-agent Communities: A Conceptual Framework

The dynamisms in open normative multi-agent communities produce changes to enacted norms where some becomes obsolete while others appear and emerge. Such changes result in obsolete norm derogation and eventually disappearance. Norm disappearance happens when the majority removes it from their belief base to avoid conflicts in the enactment of old and new norms. This paper presents a conceptual framework for norms disappearance in open normative multi-agent communities. Norms disappearance is influenced by five elements which are norms conflict; norm’s trust decay; high assimilation cost of low norm’s yields; norm’s yields decay, and violation cost decay. In addition, four parameters that affect on the values of these elements and thus on the decision of an agent to remove a norm from its cognitive structure are assimilation cost; yields value; trust value; and violation rate. Negative changes on these parameters prompt agents to think about the possibility of removing the norm.

Software analysis for Agents of Things (AoT) applications

The Agents of Things is an extension of the Internet of Things concept in which objects or things are embedded with self-reasoning intelligent software agents to provide value-added services to humans. However, a particular difficulty in designing intelligent things for the Agents of Things implementation is to determine the level of reasoning ability that corresponds to the functions of the things. To achieve this goal, we need to find an optimal match of hardware configuration with the software abilities that intelligently perform the functions of the things. In this paper, we analyze different software to identify their spectrum in optimizing self-reasoning intelligent software for the things. The result of this analysis is a software spectrum that is organized in terms of complexity and sophistication. We discuss the analytical process to arrive at the spectrum.

Identifying key factors in agent-based simulation model on processes in time-constrained environment

Processes in time-constrained environment which could be known as works or tasks with deadlines became a part of life in most areas. Efficiency of life is even measured by the ability of an individual in meeting deadlines. Due to this reality, agent-based simulation systems ventured into simulating tasks with deadlines as a measure to maximize the efficiency of work. However, due to propriety nature of the existing research works, similar research works could find no means of reusing or replicating results from the existing models developed. This paper discusses the existing research works together with limitations that are hindering the evolution of agent-based simulation modelling for tasks with deadlines. Within this paper, key factors in agent-based model for simulating tasks with deadlines are identified, which further refine the existing conceptual model that address the limitations found.

Identifying variables dependency that influences a high level deliberation process in a CI-based Multi-agent System

Intelligence is the ability of using ones knowledge in solving a given problem in the most strategic manner. The efficiency of this logical ability is influenced by the knowledge depth that the individual had comprehended throughout his life experiences. The deeper knowledge the individual has about an event, the more confident the individual becomes at approaching at a successful outcome. In this paper, we describe the variables and their influence towards the knowledge depth of an agent in a multi agent system.

A novel conflict resolution strategy in multi-agent systems: Concept and model

In this paper, we present the concept and model of a conflict resolution strategy for a multi-agent system that covers all aspects of conflict processing, from collecting agents’ opinions, recognition of possible conflict status, and through a joint final decision. Our approach is to specify a novel structure for classifying conflict states in decision-making in which related factors, such as number of conflicting agents, agent’s confidence level and strength of conflict play essential roles in guiding and selecting the conflict resolution strategies. We provide an example scenario as a proof of concept to show the model’s applicability in multiple conflict situations.

Norm’s Trust Model to Evaluate Norms Benefit Awareness for Norm Adoption in an Open Agent Community

In recent developments, norms have become important entities that are considered in agent-based systems’ designs. Norms are not only able to organize and coordinate the actions and behaviour of agents but have a direct impact on the achievement of agents’ goals. Consequently, an agent in a multi-agent system requires a mechanism that detects specific norms for adoption while rejecting others. The impact of such norms selection imposes risks on the agent’s goal and its plan ensuing from the probability of positive or negative outcomes when the agent adopts or reject some norms. In an earlier work, this predicament is resolved by enabling an agent to evaluate a norm’s benefits if it decides to adopt a particular norm. The evaluation mechanism entails a framework that analyzes a norm’s adoption ratio, yield, morality and trust, the unified values of which indicates the norm’s benefits. In this paper, the trust parameter of the mechanism is analyzed and a norm’s trust model is proposed and utilized in the evaluation of a norm’s benefits for subsequent adoption or rejection. Ultimately, the norm’s benefits are determined as a consequence of a favorable or unfavorable trust value as a significant parameter in a norm’s adoption or rejection.