Azhana Ahmad

A Dynamic Measurement of Agent Autonomy in the Layered Adjustable Autonomy Model

In a dynamically interactive systems that contain a mix of humans’ and software agents’ intelligence, managing autonomy is a challenging task. Giving an agent a complete control over its autonomy is a risky practice while manually setting the agent’s autonomy level is an inefficient approach. In this paper, we propose an autonomy measurement mechanism and its related formulae for the Layered Adjustable Autonomy (LAA) model. Our model provides a mechanism that optimizes autonomy distribution, consequently, enabling global control of the autonomous agents that guides or even withholds them whenever necessary. This is achieved by formulating intervention rules on the agents’ decision-making capabilities through autonomy measurement criteria. Our aim is to create an autonomy model that is flexible and reliable.

A conceptual model of layered adjustable autonomy

Autonomy and autonomous agents are currently the most researched topics in autonomous systems. Issues like autonomy adjustment, autonomy level, and the required degree of autonomy to be performed are investigated. Abstracting an autonomy model poses the problem of identifying specific aspects that merit an autonomous system. In this paper, we propose another model of autonomy that conceptualizes autonomy as a spectrum, which is constructed in a layered structure of a multi-agent environment called Layered Adjustable Autonomy (LAA). The autonomy spectrum of the LAA is divided into adjustable-leveled layers. Each of which has distinct attributes and properties that assist an agent in managing the influences of the environment during its decision-making process. The LAA structure is designed to endorse an agent’s qualification to make a decision by setting the degree of autonomy to the agent’s choice of decision-making. An Autonomy Analysis Module (AAM) is also proposed to control and delegate the agent’s actions at specific autonomy levels. Hence, the AAM determines the threshold of the agent autonomy level to act in its qualified layer. Ultimately, the proposed LAA model will be implemented on an air drone for the purpose of testing and refinement.

A Dynamically Adjustable Autonomic Agent Framework

The design and development of autonomous software agents is still a challenging task and needs further investigation. Giving an agent the maximum autonomous capabilities may not necessarily produce satisfactory agent behavior. Consequently, adjustable autonomy has become the hallmark of autonomous systems development that influences an agent to exhibit satisfactory behavior. To perform such influences, however, a dynamic adjustment mechanism is needed to be configured. The influences are costly in time and implementation especially for systems with time-critical domain. They might negatively influence agent decisions and cause system disturbance. In this paper, we propose a framework to govern an agent autonomy adjustment and minimize system disturbance. The main components of the proposed framework are the Planner, Scheduler and Controller (PSC) that conform to the current trends in automated systems. Two modules are also suggested which are Autonomy Analysis Module (AAM) and Situation Awareness Module (SAM). They are accordingly used to distribute the autonomy and provide balance to the system so that it’s local and global desires do not conflict.

Norms Detection and Assimilation in Multi-agent Systems: A Conceptual Approach

In this paper, we propose a technique for a software agent to detect the norms of a community of agents and assimilate its behavior to comply with the local normative protocol, failing which, the agent is refused services and resources. In this technique, the software agent is equipped with an algorithm, which detects and analyzes the normative interactions between local agents. When the detection is successful, it launches another algorithm to request for its assimilation to the local normative protocol, indicating its acceptance by the group of local agents.

Potential norms detection in social agent societies

In this paper, we propose a norms mining algorithm that detects a domain’s potential norms, which we called the Potential Norms Mining Algorithm (PNMA). According to the literature, an agent changes or revises its norms based on variables of local environment and amount of thinking about its behaviour. Based on these variables, the PNMA is used to revise the norms and identify the new normative protocol to comply with the domain’s norms. The objective of this research is to enable an agent to revise its norms without a third party enforcement unlike most of the work on norms detection and identification, which entail sanctions by an authority. We demonstrate the execution of the algorithm by testing it on a typical scenario and analyse the results on several issues.

Formulating Dynamic Agents’ Operational State via Situation Awareness Assessment

Managing autonomy in a dynamic interactive system that contains a mix of human and software agent intelligence is a challenging task. In such systems, giving an agent a complete control over its autonomy is a risky practice while manually setting the agent’s autonomy level is an inefficient approach. This paper addresses this issue via formulating a Situation Awareness Assessment (SAA) technique to assist in determining an appropriate agents’ operational state. We propose four operational states of agents’ execution cycles; proceed, halt, block and terminate, each of which is determined based on the agents’ performance. We apply the SAA technique in a proposed Layered Adjustable Autonomy (LAA) model. The LAA conceptualizes autonomy as a spectrum and is constructed in a layered structure. The SAA and the LAA notions are applicable to humans’ and agents’ collaborative environment. We provide an experimental scenario to test and validate the proposed notions in a real-time application.

An exploratory review of software agents

Along with distributed problem solving and parallel artificial intelligence, agents and multi-agent systems collectively form one of the three broad areas which fall under distributed artificial intelligence (DAI). In this paper, we explore and review the general concepts of software agent technology; definitions, types, architectures, agent communication, multiagent systems and agent development systems.

Operational Rules for Implementing Sincere Software Agents in Corrective and Preventive Actions Environment

Organizational management that manifests favourable behavioural characters among organizational members brings many benefits to the organizations and their team members. Such successful management ideas, offer software agent technology researchers a model to adopt these behaviours in software agent-based systems. In this paper, we propose operational rules for implementing sincere and sacrifice characters of agents in agent-based systems for corrective and preventive actions in quality management environments. To ensure that a sacrifice agent still produces the best services for its own increasingly progressive task, our proposed agent-based system compares the urgency and importance of its current task and an incoming task. The rules for implementing sincere and sacrifice agent help us to formulate sincere behavior in the agent-based systems.

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.