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) …
We've found the running theme for TVs at this year's CES: built-in voice assistants. Hisense has revealed that some of its 2018 4K TVs will include Amazon Alexa, letting you control both the set itself and your smart home. You can change inputs, stream online radio or turn on your lights without budging from the couch. Hisense isn't saying much about the TVs themselves, but it does note that a 100-inch laser TV will be one of those receiving the Alexa treatment.
The economic viability of a manufacturing organization depends on its ability to maximize customer services; maintain efficient, low-cost operations; and minimize total investment. These objectives conflict with one another and, thus, are difficult to achieve on an operational basis. Much of the work in the area of automated scheduling systems recognizes this problem but does not address it effectively. The work presented by this Ph.D. dissertation was motivated by the desire to generate good, costeffective schedules in dynamic and stochastic manufacturing environments (Berry 1991). Experimental analysis is used to illustrate…the PCP approach within an advanced scheduling architecture.
STRIPS (as given in Nilsson [1998, pp. I now illustrate the planning algorithm with an example from the blocks world. In the version given at the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) competition, there are four actions, given below in Although the planner returns a reasonable plan in the previous example, this might not be the case in general. For example, given the same initial situation description and the goal on(a, b), on(b, c), it returns the plan [unstack(c, b), putdown (c), unstack(b, a), putdown(b), pickup(a), stack(a, b), unstack(a, b), putdown(a), pickup(b), stack(b, c), pickup(a), stack(a,b)] which is obviously not a good one. For the AIPS'00 competition, our team implemented the following strategy for postprocessing: Remove all immediate cycles.
Most work in automatic programming has focused primarily on the roles of deduction and programming knowledge However, the role played by knowledge of the task domain seems to be at least as important, both for the usability of an automatic programming system and for the feasibility of building one which works on nontrivial problems This perspective has evolved during the course of a variety of studies over the last several years, including detailed examination of existing software for a particular domain (quantitative interpretation of oil well logs) and the implementation of an experimental automatic programming system for that domain The importance of domain knowledge has two important implications: a primary goal of automatic programming research should be to characterize the programming process for specific domains; and a crucial issue to be addressed in these characterizations is the interaction of domain and programming knowledge during program synthesis Used by permission of the International Joint Conferences on Artificial Intelligence; copies of the Proceedings are available from William Kaufmann, Inc, 95 First St., Los Altos, CA 94022 USA. For example, the work of Green (1969) and Waldinger and Lee (1969) in the late 1960s was concerned with the use of a theorem-prover to produce programs. This deductive paradigm continues to be the basis for much research in automatic programming (e.g., Manna & Waldinger 1980, Smith 1983). In the mid 1970's, work on the PSI project (Barstow 1979, Green 1977, Kant 1981) and on the Programmer's Apprentice (Rich 1981) was fundamentally concerned with the codification of knowledge about programming techniques and the use of that knowledge in program synthesis and analysis Work within the knowledge-based paradigm is also continuing (e.g., Barstow 1982, Waters 1981). This article is concerned with the role played by knowledge of the task domain, a role which seems to be at least as important.
India is a multilingual and multicultural country that came together less than a century ago. The artificial intelligence community, which gained in strength in the 1980s, has had a major focus on research directed toward societal goals of bridging the linguistic and educational divide, and delivers the fruits of information technology to all people. In this article we look at a brief history followed by two examples of research aimed at crossing the language barriers. . Artificial intelligence in India has been pursued by a passionate few over the last few decades. It has not been as widespread as in Europe and the USA.
This article is my personal account on the work at Stanford on Stanley, the winning robot in the DARPA Grand Challenge. Between July 2004 and October 2005, my then-postdoc Michael Montemerlo and I led a team of students, engineers, and professionals with the single vision of claiming one of the most prestigious trophies in the field of robotics: the DARPA Grand Challenge (DARPA 2004). The Grand Challenge, organized by the U.S. government, was unprecedented in the nation's history. It was the first time that the U.S. Congress had appropriated a cash price for advancing technological innovation. My team won this prize, competing with some 194 other teams.
A performance evaluation of 15 text-analysis systems was recently conducted to realistically assess the state of the art for detailed information extraction from unconstrained continuous text. Reports associated with terrorism were chosen as the target domain, and all systems were tested on a collection of previously unseen texts released by a government agency. Based on multiple strategies for computing each metric, the competing systems were evaluated for recall, precision, and overgeneration. The results support the claim that systems incorporating natural language-processing techniques are more effective than systems based on stochastic techniques alone. A wide range of language-processing strategies was employed by the top-scoring systems, indicating that many natural language-processing techniques provide a viable foundation for sophisticated text analysis.
A novel approach is presented for the development of expert systems for structural design problems. This approach differs from the conventional expert systems in two fundamental respects. First, mathematical optimization is introduced into the design process. Second, a computer is used to obtain parts of the knowledge necessary in the expert systems in addition to heuristics and experiential knowledge obtained from documented materials and human experts. As an example of this approach, a prototype coupled expert system, the bridge truss expert (BTExpert), is presented for optimum design of bridge trusses subjected to moving loads.
We describe an application of a dynamic replanning technique in a highly dynamic and complex domain: the military aeromedical evacuation of patients to medical treatment facilities. U.S. Transportation Command (USTRANSCOM) is the U.S. Department of Defense (DoD) agency responsible for evacuating patients during wartime and peace. Doctrinally, patients requiring extended treatment must be evacuated by air to a suitable medical treatment facility. The Persian Gulf War was the first significant armed conflict in which this concept was put to a serious test. The results were far from satisfactory--about 60 percent of the patients ended up at the wrong destinations.
Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are parking a car, driving in city traffic, playing golf, cooking a meal, and summarizing a story. In performing such tasks, humans use perceptions of time, direction, speed, shape, possibility, likelihood, truth, and other attributes of physical and mental objects. Reflecting the bounded ability of the human brain to resolve detail, perceptions are intrinsically imprecise. In more concrete terms, perceptions are f-granular, meaning that (1) the boundaries of perceived classes are unsharp and (2) the values of attributes are granulated, with a granule being a clump of values (points, objects) drawn together by indistinguishability, similarity, proximity, and function.