Artificial Intelligence: The Heart Of Artificial Intelligence

Ai) People communicate for many different reasons. One of the main reasons that people communicate is to understand each other. Without the ability to communicate nobody would understand what is expected of them and we wouldn’t know the needs of others. People also communicate to share their wants, needs and feelings. In order for us to adequately care for someone we need to know and understand what they expect from us and how they feel about different situations. Without communication we wouldn’t be able to have a conversation therefore wouldn’t know anybody’s likes or dislikes. We communicate to give and receive support and to express our thoughts, ideas and information. By doing all of this we also make and develop

Artificial Intelligence (AI) gives machines the power to behave like humans. AI enables the machines to make decisions based on gathered data. An intelligent agent must be able to reproduce the cognitive functions accomplished by humans, which includes: learning, reasoning, recognizing context, perception, linguistic intelligence and problem solving. A Chatbot or Chatterbot or Talkbot is a conversational program that engages with humans using natural language. Communication can either be textual or auditory.

Coloured by popular culture we see AI as this sci-fi fantasy of an independent thinking machine capable of making highly intellectual decisions and sometimes usurping control and power over humanity. Though fantastic prediction as it sounds and, the tune often played by press, AI has a lot to go before reaching an intelligent Strong AI. Futurist and inventor Ray Kurzweil describes, that by laws of accelerating returns the advancements in AI will have a compounding effect thus generating progress at an exponential pace. Neural networks and Reinforcement Learning, the engines behind the AI have already outsmarted humans in games like Go and Jeopardy. These highly tailored technologies are all around us and we just don’t realise, as John McCarthy

Intelligent agents are the entities which get the input from all the sensors and perform specific job using actuators (A type of motor which performs movement). Figure 1.1 depicts the general idea of the intelligent agents. The main goal of intelligent agents is to have same intellectual thinking as humans and they should exhibit their intelligence in different domains. Multi-Agent systems framework is used for the creation of intelligent agents in which different modules are used for the different facets of an intelligent system and each module communicate with every other module.

Artificial intelligence is intelligence exhibited by machines. In computer science terms it artificial intelligence can be interpreted as any device that perceives its environment and takes an action that maximizes its chance of success at some specific goal. Consequently, the term artificial intelligence is applied when a machine is able to accomplish cognitive functions that humans associate with human skill such as learning and problem solving (Stuart et al). As computers have become exceedingly faster and more efficient, artificial intelligence has also rose to the forefront and is being implemented in technologies varying from autonomous cars to chess games and is increasingly becoming “smarter” as time progresses (Stuart et al).

The purpose of this section is to provide the reader with a brief insight on Embodied Conversational agents ( ECAs). This chapter is organized into three section. The First section gives a general overview about ECAs through literature review. The second Section explores some concerns related to the use of agent in different contexts. The third section considers the design decision’s perspectives of virtual agents

Artificial Intelligence: Cognitive Ability or Information Processing Computers have become an integral part of our everyday lives. We rely upon these machines to perform innumerable tasks that we often take for granted. Most people realize that computers are able to perform the multitude of functions as a consequence of the programming they receive. These programs give computers a set of instructions that governs their transition from one information processing state to another. Thus, computational machines are able to respond to a certain set of inputs with a certain range of outputs. In order to comprehend programs one needs only to describe these instructions in functional terms. In this regard, computer programs are extremely similar

Above mentioned examples, suggest the trend of games being built around self-adaptive Game AI as their core concept. On the other hand, there are other uses of using this type of Game AI, such as instead of being used as a gameplay mechanic, it is used to simulate the role of a player, here the player relinquishes complete or partial control over to AI to play a game. Some examples of this type of usage, are mentioned below:

Department of Computer Science Gettysburg College Campus Box 402 Gettysburg, PA 17325-1486 Introduction As computer gaming reaches ever-greater heights in realism, we can expect the complexity of simulated dynamics to reach further as well. To populate such gaming environments with agents that behave intelligently, there must be some means of reasoning about the consequences of agent actions. Such ability to seek out the ramifications of various possible action sequences, commonly called “lookahead”, is found in programs that play chess, but there are special challenges that face game programmers who wish to apply AI search techniques to complex continuous dynamical systems.

Engineers today are closer than ever to creating an artificially intelligent machine but, humanity lacks concrete resolutions to crucial questions regarding the treatment of intelligent beings. Ethics have, unfortunately, been an afterthought throughout in most of these regulations and restrictions. This paper will highlight many unresolved questions in roboethics and offer some solutions. It is essential that computer scientists resolve as many ethical conflicts as possible before the inevitable arrival of true AI.

The general game players require the designed agents are able to learn diverse game-playing strategies without any game-specific knowledge, which shows that it could rapidly adopt to a rationally given environment with comparatively more general intelligences (Thielscher 2011, p.1). Further more, a successful GGP player should enable the system to analyze the task by itself. The GGP intelligence not only relies on the programmer, but also game player. For better performance, the design of general game players ought to combine different discipline and work in a synergic way. It should have able to play arbitrary games from simple game of Connect Four to complex board game that can be

18). All the subtle differences for understanding the text-based messages that comes from the HTTP Server will be hides away by the Game-Agent Interface (Finnsson 2012, pp. 17-18). Runtime information that associates to the player and statistical data will also be recorded by Game-Agent Interface between moves through special functions (Finnsson 2012, p. 18).

The evolution of the agent state is described as follows: starting with an initial situation, at each step one agent i is chosen randomly and the local densities, σi,l (l=X, Y), is computed for each of the language states in i’s neighbourhood. The probabilities of an agent switching its state is according to the following formula.

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).

A multi-agent based system is a powerful modeling technique for simulating individual interactions in a dynamic system and is distinctive in its ability to simulate situations with unpredictable behavior