If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The automotive industry was the first to promote the development of applications of model-based systems technology on a broad scale and, as a result, has produced some of the most advanced prototypes and products. In this article, we illustrate the features and benefits of model-based systems and qualitative modeling by prototypes and application systems that were developed in the automotive industry to support on-board diagnosis, design for diagnosability, and failure modes and effects analysis. Car manufacturers and their suppliers face increasingly serious challenges particularly related to fault analysis and diagnosis during the life cycle of their products. On the one hand, the complexity and sophistication of vehicles is growing, so it is becoming harder to predict interactions between vehicle systems, especially when failures occur. On the other hand, legal regulations and the demand for safety impose strong requirements on the detection and identification of faults and the prevention of their effects on the environment or dangerous situations for passengers and other people.
The t.estbed simulates a class of a distributed knowledge-based THERE ARE TWO MAJOR T IEMES of this article. First, WC introduce readers to the emerging subdiscipline of AI called Dzstrrbuted Problem Solving, and more specifically the authors' research on Functionally Accurate, Cooperative systems Second, we discuss the st,ructure of tools that allow more thorough experimentation than has typically been performed in AI research An examplr of such a tool, the Distributed Vehicle Monitoring Testbed, will bc presented. The testbed simulates a class of dist,ributed knowledge-based problem solving systems operating on an abstracted version of a vehicle monitoring task. This presentation emphasizes how the t,estbed is structured to facilit,ate the study of a wide range of issues faced in t,he design of distributed problem solving networks. Distribut,ed Problem Solving (also called Distributed Al) combines the research interests of the fields of AI and Distributed Processing (Chandrasekaran 1981; Davis 1980, 1982; Fehling & Erman 1983).
Now completing its first year, the High-Performance Knowledge Bases Project promotes technology for developing very large, flexible, and reusable knowledge bases. The project is supported by the Defense Advanced Research Projects Agency and includes more than 15 contractors in universities, research laboratories, and companies. Programs lack knowledge about the world sufficient to understand and adjust to new situations as people do. Consequently, programs have been poor at interpreting and reasoning about novel and changing events, such as international crises and battlefield situations. These problems are more open ended than chess.
An adequate natural language description of developments in a real-world scene can be taken as proof of "understanding what is going on." An algorithmic system that generates natural language descriptions from video recordings of road traffic scenes can be said to "understand" its input to the extent that algorithmically generated text is acceptable to the humans judging it. The ability to present a "variant formulation" without distorting the essential parts of the original message is taken as a cue that these essentials have been "understood." During art lessons, in particular those concerned with classical or ecclesiastic paintings, students are initially invited to merely describe what they see. Frequently, considerable a priori knowledge about ancient mythology or biblical traditions is required to succinctly characterize the depicted scene. Lack of the corresponding knowledge about other cultures can make it difficult for someone with only a European education to really understand and describe in an appropriate manner a painting by, for example, a Far East classic artist. Familiar human experiences mentioned in the preceding paragraph will now be "morphed" into a scientific challenge: to design and implement an algorithmic engine that generates an appropriate textual description of essential developments in a video sequence recorded from a real-world scene. Such an algorithmic engine will serve as one example of a cognitive vision system (CVS), which leaves room, as the experienced reader has noticed, for there to be more than one way to introduce the concept of a CVS. An alternative clearly consists in coupling a computer vision system with a robotic system of some kind and assessing the reactions of such a compound system. To whomever accepts the formulation, "one of the actions available to an agent is to produce language. This is called a speech act. Russell and Norvig (1995)" is unlikely to consider the two variants of a CVS alluded to previously as being fundamentally different. With regard to the first CVS version in particular, the following remarks are submitted for consideration: Obviously, we avoid a precise definition of understanding in favor of having humans compare the reaction of an algorithmic engine to that expected from a human. This fuzzy approach toward the circumscription of a CVS opens the road to constructive criticism--that is, to incremental system improvement--by pinpointing aspects of an output text that are not yet considered satisfactory.
Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Since 1987, researchers at Carnegie Mellon University have been investigating one such task. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.
In this article, we show how 4D/RCS incorporates and integrates multiple types of disparate knowledge representation techniques into a common, unifying architecture. The 4D/RCS architecture is based on the supposition that different knowledge representation techniques offer different advantages, and 4D/RCS is designed in such a way as to combine the strengths of all of these techniques into a common unifying architecture in order to exploit the advantages of each. In the context of applying the architecture to the control of autonomous vehicles, we describe the procedural and declarative types of knowledge that have been developed and applied and the value that each brings to achieving the ultimate goal of autonomous navigation. We also look at symbolic versus iconic knowledge representation and show how 4D/RCS accommodates both of these types of representations and uses the strengths of each to strive towards achieving human-level intelligence in autonomous systems. Neuroanatomy has described the structure and function of the basic computational element of the brain--the neuron--and produced extensive maps of the computational modules and interconnecting data flow pathways making up the anatomy of the brain. Behavioral psychology provides information about stimulus-response behavior and instrumental conditioning. Cognitive psychology is exploring how the brain represents knowledge; how it perceives objects, events, situations, and relationships; how it analyzes the past and plans for the future; and how it selects and controls behavior that satisfies desires and achieves goals Over the last five decades, the invention of the electronic computer has brought rapid advances in computational power, making it feasible to launch serious attempts at building intelligent systems. Artificial intelligence and robotics have produced significant results in planning, problem solving, rule-based reasoning, image analysis, and speech understanding. Autonomous vehicle research has produced advances in real-time sensory processing, world modeling, navigation, path planning, and obstacle avoidance. Research in industrial automation and process control has produced hierarchical control systems, distributed databases, and models for representing processes and products. Modern control theory has developed precise understanding of stability, adaptability, and controllability under various conditions of uncertainty and noise. Progress is rapid in each of the above fields, and there exists an enormous and rapidly growing body of literature in all of these areas.
This problem is particularly pronounced for operations planners and controllers, who must be very highly knowledgeable and experienced with the business domain. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision making and problem solving so that its planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission-critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint-satisfaction problem (CSP). The use of AI enabled the travel agency to sign up additional hotel partners, handle more orders, and expand its fleet with its existing team of planners and controllers.
A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers. In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine-learning algorithms with demographic information and expert advice into existing automated assistive systems.
J. Sainsbury has extensive assets, with subsidiaries such as Shaws in the United States and the Savacentre and Homebase chains in the United Kingdom. Given J. Sainsbury's position in the retail market, the efficient and effective running of the supply chain for J. Sainsbury is critical to the mission of the organization. The J. Sainsbury logistics purpose statement is to manage the flow of goods from supplier to shelf, ensuring that the customer has the right product in the right place at the right time. To these ends, J. Sainsbury's Logistics Group is committed to being world class. The group's direction principle is to be seen as the world's best logistics team.
Many have used Kalman filter techniques based on the equations of vehicle motion; these techniques most accurately predict shortterm motion. With intelligent path prediction, the long-term mission objective of the vehicle is being predicted in addition to the short-term motion. Thus, when applied to predicting the motion of a car, an intelligent predictor will attempt to predict the final destination--say, for example, the vehicle appears to be going to the post office or the art museum--in addition to predicting which streets will be used. The theory is also applicable to predicting air vehicle travel, so that for a military application, the target (from a set of plausible targets) and the threat-avoidance policy (from a set of plausible policies), in addition to the route, can be predicted. The first investigation is to develop a method for identifying a decisionmaking strategy that seemingly explains the vehicle's motion.