Designing adaptive artificial agents for an economic production and conflict model

Production and conflict models have been used over the past 30 years to represent the effects of unproductive resource allocation in economics. Their major applications are in modelling the assignment of property rights, rentseeking and defense economics. This paper describes the process of designing an agent used in a production and conflict model. Using the capabilities of an agent-based approach to economic modelling, we have enriched a simple decision-maker of the kind used in classic general equilibrium economic models, to build an adaptive and interactive agent which uses its own attributes, its neighbors’ parameters and information from its environment to make resource allocation decisions. Our model presents emergent and adaptive behaviors than cannot be captured using classic production and conflict agents. Some possible extensions for future applications are also recommended.

Flock inspired area coverage using wireless boid-like sensor agents

Simulated flocking is achievable using three boid rules [13]. We propose an area coverage model inspired by Reynolds’ flocking algorithm, investigating strategies for achieving quality coverage using flocking rules. Our agents are identical and autonomous, using only local sensory information for indirect communication. Upon deployment, agents are in the default separation mode. The cohesion rule would then guarantee that agents remain within the swarm, covering spaces with explored neighbour spaces. Four experiments are conducted to evaluate our model in terms of coverage quality achieved. We firstly investigate agents’ separation speed before the speed with which isolated agents re-organizes is investigated. The third experiment compares coverage quality achieved using our model with coverage quality achieved using random guessing. Finally, we investigate fault tolerance in the event of agents’ failures. Our model exhibits good separation and cohesion speed, achieving high quality coverage. Additionally, the model is fault tolerant and adaptive to agents’ failures.

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).

Electronic negotiation and security of information exchanged in e-commerce

In settings such as electronic markets where trading partners have conflicting interests and a desire to cooperate, mobile agent mediated negotiation have become very popular. However, agent-based negotiation in electronic commerce involves the exchange of critical and sensitive data that must be highly safeguarded. Therefore, in order to give benefits of quick and safe trading to the trading partners, an approach that secures the information exchanged between the mobile agents during e-Commerce negotiations is needed. To this end, we discuss an approach that we refer to as Multi-Agent Security NEgotiation Protocol (MASNEP). To show that MASNEP protocol is free of attacks and thus the information exchanged throughout electronic negotiation is truly secured, we provide a formal proof on the correctness of the MASNEP.

Secure mobile agent-based e-negotiation for on-line trading

Electronic negotiation (e-negotiation) is a major activity in e-Commerce applications. Agent-based e-negotiation has recently received increasing attention. However, agent-based electronic negotiation suffers from a number of security attacks. In this paper, we present a mobile agent-based e-commerce framework. We also propose a security protocol that protects the information exchanged between the mobile agents during e- negotiations. We reason the correctness of the proposed security protocol in the presence of various security threats. The reasoning shows that the protocol maintains privacy, non- repudiation, authenticity, anonymity, and strong integrity of exchanged information.

A probabilistic movement model for shortest path formation in virtual ant-like agents

We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.

Adaptive data based neural network leader-follower control of multi-agent networks

In this paper, we propose a data based neural network leader-follower control for multi-agent networks where each agent is described by a class of high-order uncertain nonlinear systems with input perturbation. The control laws are developed using multiple-surface sliding control technique. In particular, novel set of sliding variables are proposed to guarantee leader-follower consensus on the sliding surfaces. Novel switching is proposed to overcome the unavailability of instantaneous control output from the neighbor. By utilizing RBF neural network and Fourier series to approximate the unknown functions, leader-follower consensus can be reached, under the condition that the dynamic equations of all agents are unknown. An O(n) data based algorithm is developed, using only the network’s measurable input/output data to generate the distributed virtual control laws. Simulation results demonstrate the effectiveness of the approach.

A multi-agent incremental negotiation scheme for meetings scheduling

We present an agent-oriented approach to the meeting scheduling problem and propose an incremental negotiation scheme that makes use of a hierarchical structure of an individual agent's working knowledge. First, we formalise the meeting scheduling problem in a multi-agent context, then elaborate on the design of a common agent architecture of all agents in the system. As a result, each agent becomes a modularised computing unit yet possesses high autonomy and robust interface with other agents. The system reserves the meeting participants' privacy since there are no agents with dominant roles, and agents can communicate at an abstract level in their hierarchical structures

Information systems development projects as complex adaptive systems

This research considers information systems development (ISD) projects as complex adaptive systems. We investigate the question whether complex adaptive systems (CAS) theory is relevant as a theoretical foundation for understanding ISD, and if so, which kind of understanding can be achieved by utilizing the theory? We introduce key concepts of CAS theory such as interaction, emergence, interconnected autonomous agents, selforganization, co-evolution, poise at the edge of chaos, time pacing, and poise at the edge of time to analyse and understand ISD in practice. We demonstrate the strength of such a CAS approach through an empirical case study presentation and analysis. While our work contributes to a complexity theory of ISD, the case examination also provides practical advice derived from this perspective to successfully cope with complexity in ISD in an adaptive manner.

Mobile-agent based multi-constraint one-to-many bilateral e-Negotiation framework

The thesis proposes a multi-constraint one-to-many bilateral e-Trade negotiation framework. It deploys mobile agents in negotiation, considers trading competition between vendors and search space, efficiently manages the risk of losing top utility offers that expire before the negotiation deadline, accurately evaluates offers, and truly maintains the security of negotiation data.

Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.