General Features Of Intelligent Agents

Artificial Intelligence (AI) is the branch of computer science, which concentrates on the intelligence of machines, and involves applying the principles of reasoning, knowledge planning, learning, communication, perception, and controlling objects to emulate the human brain. The most recognizable AI application is robotics from Hollywood cinema, and includes films such as; I Robot, Transformers, Wall-e, WarGames, A.I., The Terminator, Robocop, Iron Man, and Star Wars to name a few, which is fictional not an actual representation of AI. Robotics applications is only one of three aspects of AI, which also includes

The purpose of this paper is to bring to light a fresh new perspective of Artificial Intelligence or simply (AI). There have been numerous endeavours to make artificial intelligence which is inclusive of frontiers such as neural network, evolution theory, and so forth, not forgetting that a number of current issues have found solutions in the application of these concepts, the case still remains that each theory only covers a certain isolated aspect of human intelligence. To date, he gap that stands between a human being and an artificial intelligence agent still remains unabridged. In this paper an extrapolated version of artificial intelligence shall be discussed which will be augmented by emotions and the plausibility of inheriting a neural architecture from one generation to the next in a bid to make artificial intelligence to compare to the natural behaviour and intelligence of human

Taking into account this idea, we developed agent-based model that is intended to capture individual

Artificial intelligence is the development of a computer system that is able to perform tasks of human intelligence like visual perception, speech recognition, and decision-making. Computer scientists have made a substantial advancement in the

It is practically impossible for a nation’s intelligence community to detect and prevent every attack on its soil. However, the American failure to uncover the 9/11 plot should be considered a massive failure considering the scope of the attack which involved careful planning, training many perpetrators, and a high profile target with many victims. Furthermore it can be demonstrated that there were many signals and opportunities to discover the plot that the intelligence machinery missed. “Hindsight is 20/20” and there needs to be an understanding of how easy it is to look back at how the intelligence gathered before the attacks should have signaled a serious threat, and thus provoked the unraveling of the 9/11 plot had the intelligence community picked up on and discriminated the signals from the noise. . However, there is evidence that the US intelligence community’s failure to link the information they had differentiated from the “background noise” as representing a possible terrorist threat points to the entire failure of the intelligence system. Therefore, while a nation’s intelligence system and the communities supporting it cannot be expected to succeed 100% of the time, this incident pointed at a 100% failure of the American intelligence community. Although it is recognized that many factors contributed to this failure, there is evidence that the organizational structure and culture both within the individual intelligence agencies and amid the intelligence

The complexity of artificial intelligence tasks has grown steadily, agent-based systems technology has been hailed as a new paradigm for automating, conceptualizing, designing, and implementing such software systems. A multi-agent system is a computerized system composed of multiple interacting intelligent agents within an dynamic environment. Agents are intelligent computer programs that act autonomously on behalf of

The first step in this project is a task analysis (Newell & Simon, 1972). In this step we take the problem in Renkl et al.'s study and figure out exactly what the correct procedure is in solving the problems. This is one of the most critical steps in doing this project as we must correctly identify each piece of the solution of the problem. One of the goals of a good cognitive model is to accurately solve the problem as a real person would solve it; we don't want to just solve it as best we can. The result of the CTA will be a target knowledge base summarizing expert knowledge for this domain.

Artificial intelligence, or AI, is a field of computer science that attempts to simulate characteristics of human intelligence or senses. These include learning, reasoning, and adapting. This field studies the designs of intelligent

In this report, I am going to talk about what is mental model and explore it how applied in HCI (Human computer interaction). Ordering to provide more compelling evidence and data to analysis mental model, an experiment about personal mental model build will be developed. In the end, a conclusion about this report also will be present. Reference list and appendix for this report will be placed in the last part.

One of this AI technologies tools is the Artificial Neural Networks which work much like the human brain and have the ability to learn from training

To cater the need of computer animation, interactive media such as games and virtual reality there is need for intelligent agent, those who can navigate according to requirement. These agents may have some ability to improvise their actions according to situation. This project will use the term intelligent to denote these agents which are intelligent agents in a virtual world who may be interacting with other agents. Intelligent agents are intended for use in computer animation and various interactive media such as games and virtual reality. This project will use the term virtual to denote these agents which, rather than being simulations of an electrical device in the real world, are instead intelligent agents in a virtual world.

Based on the prelude, we may define an agent as “An autonomous, reactive, pro-active computer system, typically with a central locus of control that is at least able to communicate with other agents via some kind of communication language” (Weiss, 1999)

In multi agent systems, there are four basic elements that collaborate to build the design of the system. The first element is agents, which composes of group of autonomous agents that are considered the first and main part of the internal structure of the MAS design (Manzoni, 2009). Those agents are different in their numbers, method of response, smartness, dealing with problems, and internal architecture in general. Since agents are necessary to interact and cooperate with each other to find a solution for common tasks or uncommon tasks as well, by communicating via special messages that can arrive at any time. Hence, every one of these agents must be based on particular information in regarding of its actions and making decisions (Wooldrige, 2009).

Do humans and computers really process information similarly? My essay was inspired when I read how early psychologists and scientist used the newly invented computer to explain the complexity and processes of the brain. This was the beginning of cognitive psychology. Before I compare the differences and similarities between computer and humans on information processing, it is only necessary to highlight the history of cognitive psychology and the information processing model.

As we all know, it’s not the smartest people that are the most successful and the most fulfilled in life. We probably know people who are academically outstanding and yet are socially ineffective and not successful at workplace or in their personal life. Intellectual intelligence (IQ) isn’t enough for success in life. We come across people, either at workplace or in our personal lives, who are good observers. Irrespective of the situation we're in, they always seem to know exactly what to say – and how to say it – so that we're not upset or offended. They're caring and understanding, and even if we don't find a desired solution to our problem, it usually leaves optimistic feelings.