Applying artificial intelligence to virtual reality: Intelligent virtual environments
Research into virtual environments on the one hand and artificial intelligence and artificial life on the other has largely been carried out by two different groups of people with different preoccupation and interests, but some convergence is now apparent between the two fields. Applications in which activity independent of the user takes place- involving crowds or other agents- are beginning to be tackled, while synthetic agents, virtual humans, and computer pets are all areas in which techniques from the two fields require strong integration. The two communities have much to learn from each other if wheels are not to be reinvented on both sides. The reviews the issues arising from combining artificial intelligence and artificial life techniques with those of virtual environments to produce just such intelligent virtual environments. The discussion is illustrated with examples that include environments providing knowledge to direct or assist the user rather than relying entirely on the user's knowledge and skills.
Intelligence: Real or artificial?
Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. That workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selections model instead. Such a strategic change should lead them to the science of behavior analysis.
Human Wayfinding, Environment-Behavior Relationships, and Artificial Intelligence
Human wayfinding is a goal-directed process of determining routes through an unfamiliar environment. Understanding wayfinding behavior has important implications for our ability to explain and predict spatial behavior and for our attempts to plan and design the built environment for human use. In the reviews research on wayfinding in built environments. Two distinct yet complementary research perspectives are discussed: the environment-behavior and the artificial intelligence approaches to understanding wayfinding. Closer linkages between these perspectives have implications at both the theoretical and applied levels.
Imagining the thinking machine: Technological myths and the rise of artificial intelligence
This article discusses the role of technological myths in the development of artificial intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: The creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on AI published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers and technologists, three dominant patterns in the construction of the AI myth are identified:
(1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies.
(2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome
(3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth.
Type of Artificial Intelligent
1-Practical Swarm Optimization (PSO)
2- Fuzzy Logic Control (FLC).
3- Artificial Neural Network (ANN).
4- Artificial Neural Fuzzy Inference System (ANFIS)
5- Artificial Bee Colony (ABC)