The SGIA and the Common Growing Language

Human or virtual agents are presented in our lives daily. They serve our purposes and represent us in different many situations. Nowadays the number of virtual agents is increasing daily because they are cheaper, faster and more accurate than human agents. Our aim in this article is to define a new type of intelligent agent called SGIA – Self Growing Intelligent Agent and a new defining language for it. The SGIA agent is an intelligent agent with all the common agents’ characteristics and with other special one: that to learn and grow by itself in knowledge and size.Software Agent, Knowledge Management, Education Process, Language Development

Intelligent agent simulator in massive crowd

Crowd simulations have many benefits over real-life research such as in computer games, architecture and entertainment. One of the key elements in this study is to include elements of decision-making into the crowd. The aim of this simulator is to simulate the features of an intelligent agent to escape from crowded environments especially in one-way corridor, two-way corridor and four-way intersection. The addition of the graphical user interface enables intuitive and fast handling in all settings and features of the Intelligent Agent Simulator and allows convenient research in the field of intelligent behaviour in massive crowd. This paper describes the development of a simulator by using the Open Graphics Library (OpenGL), starting from the production of training data, the simulation process, until the simulation results. The Social Force Model (SFM) is used to generate the motion of agents and the Support Vector Machine (SVM) is used to predict the next step for intelligent agent

Descartes’ Intelligent Agent

Neither the natural science explanation about the agent nor the modern and postmodern philosophical interpretation of the agent (subject) have sounded the death knell of the metaphysical subject in the era of information revolution. The artificial intelligence technology represented by Microsoft Avatar Framework has spread to the creative fields such as music and literature, which are unique to human beings, showing a broad application prospect and arousing academic reflection on human subject. Despite the numerous advantages posed intelligent agents, it is neither desirable nor possible to pull out the subject’s power supply and give up AI or give up human social intelligence. Rethinking Descartes' theory of mind and body will lead to in-depth development of AI

Describing intelligent agent behaviors

The development of new intelligent agents requires an interdisciplinary approach to programming. The initial challenge is to describe the desired agent behaviors and abilities without necessarily committing the agent development project to one particular programming language. What are the appropriate linguistic and logical tools for creating a top level, unambiguous, program-independent, and consistent description of the functions and behaviors of the agent? And how can that description then be translated easily into one of a number of program languages? This article provides a case study of the application of a simple Belief, Desire, and Intention (EDI) first order logic to a complex set of agent functions of a theoretical community of intelligent nano-spacecraft. The basic research was conducted at NASA-GSFC (Greenbelt), Advanced Architecture Branch, during the summer of 2001. The simple examples of applied BDI logic presented here suggest broad application in agent software development

Simulating the Investor’s Behavior in Stock Market with an Intelligent Agent

In this paper, from the investor’s perspective, we will use intelligent agents to simulate the investor’s behaviors. The intelligent agent will learn the investor’s behavior according to investor’s historical data. It will learn from the decisions making for the investment. And we will build a virtual stock market in which intelligent agents can invest according to their decisions. Intelligent agents’ investment decisions are different due to different consideration and conclusion for the same message. Then, every intelligent agent will deal with these messages. We will train intelligent agent with data mining technology and artificial neural network to make the intelligent agent’s behaviors similar to investor’s behaviors in the real world. The intelligent agent makes decisions and invests in the virtual stock market

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to dfferent collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents other jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a multi-agent system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead policies instead of a myopic policy (10-20%) and (ii) the joint effect of two look-ahead policies is larger than the effect of an individual policy. To provide an indication of the savings that might be realized with a central solution methodology, we benchmark our results against an integer programming approach

Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system