Aug 27, · Artificial Intelligence (referred to hereafter by its nickname, “AI”) is the subfield of Computer Science devoted to developing programs that enable computers to display behavior that can (broadly) be characterized as intelligent. [] Most research in AI is devoted to fairly narrow applications, such as planning or speech-to-speech translation in limited, well defined task domains Students will cover key topics such as data processing and data analysis using modern methods that rely on Machine Learning (ML) and Artificial Inteligence (AI); surgical data acquisition, including an understanding of the devices used to capture surgical data (e.g. trackers, robots, imaging devices); robot kinematics, motion planning, control and navigation An accredited MSc in Control, Automation and Artificial Intelligence can help you to develop skills that would be beneficial in almost every engineering field from automotive, aircraft industry, power and energy, automation, process industry including oil and gas,
Electrical Engineering and Computer Science Courses – Bulletin
But substantial interest remains in the long-range goal of building generally intelligent, autonomous agents, [ 2 ] even if dissertation on artificial intelligence regarding robots goal of fully human-like intelligence is elusive and is seldom pursued explicitly and as such.
Throughout its relatively short history, AI has been heavily influenced by logical ideas. AI has drawn on many research methodologies: the value and relative importance of logical formalisms is questioned by some leading practitioners, and has been debated in the literature from time to time. The relations between AI and philosophical logic are part of a larger story.
Implicatures, for instance, have to correspond to inferences that can be carried out by a rational interpreter of discourse. Whatever causality is, causal relations should be inferrable in everyday common sense settings. Whatever belief is, it should be possible for rational agents to make plausible inferences about the beliefs of other agents. In each of these cases, compatibility with an acceptable account of the relevant reasoning is essential for a successful philosophical theory.
But the methods in the contemporary philosophical inventory are too crude to provide anything like an adequate account of reasoning that is this complex and this entangled in broad world knowledge. Bringing an eclectic set of conceptual tools to the problem of idealized reasoning in realistic settings, and using computers to model and test the theories, research in AI has transformed the study of reasoning—especially of practical, common sense reasoning. The new insights and theories that have emerged from AI are of great potential value in informing and constraining many areas of philosophical inquiry.
The special case of philosophical logic that forms the theme of this article may provide support for the more general point. Although logic in AI grew out of philosophical logic, in its new setting it has produced new theories and ambitious programs that would not have been possible outside of a community devoted to building full-scale computational models of rational agency.
This entry assumes an audience consisting primarily of philosophers who have little or no familiarity with AI. The entry concentrates on the issues that arise when logic is used in understanding problems in intelligent reasoning and guiding the design of mechanized reasoning systems.
Logic in AI is by now a very large and not very well demarcated field—nothing like complete coverage has been achieved here.
Sections 3 and Section 4 provide an overview with some historical and technical details concerning nonmonotonic logic and reasoning about action and change, a topic that is not only central in AI but that should be of considerable dissertation on artificial intelligence regarding robots to philosophers. The remaining sections provide brief and more or less inadequate sketches of selected topics, with references to the primary literature.
Minker b is a comprehensive collection of survey papers and original contributions to the field of logic-based AI, with extensive references to the literature. This volume is a good beginning point for readers who wish to pursue this topic further.
Davis a and Mueller a are book-length treatments of the challenging problem of formalizing commonsense reasoning. Antonelli a is a good entry point for readers interested in nonmonotonic logic, and Shanahan a is a useful discussion of the frame problem. Wooldridge a deals with logical formalizations of rational agents. Theoretical computer science developed out of logic, the theory of computation if this is to be considered a different subject from logicand some related areas of mathematics.
Computer scientists in general are familiar with the idea that logic provides techniques for analyzing the inferential properties of languages, and with the distinction between a high-level logical analysis of a reasoning problem and its implementations.
Logic, for instance, can provide a specification for a programming language by characterizing a mapping from programs to the computations that they license. A compiler that implements the language can be incomplete, or even unsound, as long as in some sense it approximates the logical specification. This makes it possible for the involvement of logic in AI applications to vary from relatively weak uses in which the logic informs the implementation process with analytic insights, to strong uses in which the implementation algorithm can be shown to be sound and complete.
In some cases, a working system is inspired by ideas from logic and then acquires features that at first seem logically problematic but can later be explained by developing new ideas in logical theory.
This sort of thing has happened, dissertation on artificial intelligence regarding robots, for instance, in logic programming. In particular, logical theories in AI are independent from implementations. They can be used to provide insights into the reasoning problem without directly informing the implementation.
Direct implementations of ideas from logic—theorem-proving and model-construction techniques—are used in AI, but the AI theorists who rely on logic to model their problem areas are free to use other implementation techniques as well, dissertation on artificial intelligence regarding robots. Thus, in Moore b Chapter 1Robert C.
Moore distinguishes three uses of logic in AI; as a tool of analysis, as a basis for knowledge representation, dissertation on artificial intelligence regarding robots, and as a programming language, dissertation on artificial intelligence regarding robots. A large part of the effort of developing limited-objective reasoning systems goes into the management of large, complex bodies of declarative information.
It is generally recognized in AI that it is important to treat the representation of this information, and the reasoning that goes along with it, as a separate task, with its own research problems. The evolution of expert systems illustrates the point. Later generation expert systems show a greater modularity in their design.
A separate knowledge representation component is useful for software engineering purposes—it is much better to have a single representation of a general fact that can have many different uses, since this makes the system easier to develop and to modify.
And this design turns out to be essential in enabling these systems to deliver explanations as well as mere conclusions. In response to the need to design this declarative component, a subfield of AI known as knowledge representation emerged during the s.
Knowledge representation deals primarily with the representational and reasoning challenges of this separate component. The best place to get a feel for this subject is the proceedings of the meetings that are now held every other year: see Brachman et al. Typical articles in the proceedings of the KR and Dissertation on artificial intelligence regarding robots conferences deal with the following topics. These topics hardly overlap at all with the contents of the Journal of Symbolic Logicthe principal research archive for mathematical logic.
But there is substantial overlap in theoretical emphasis with The Journal of Philosophical Logicwhere topics such as tense logic, epistemic logic, logical approaches to practical reasoning, dissertation on artificial intelligence regarding robots, belief change, and vagueness account for a large percentage of the contributions.
Very few JPL publications, however, deal with complexity theory or with potential applications to automated reasoning. A history of philosophical logic is yet to be written. Though philosophical logic has traditionally been distinguised from mathematical logic, the distinction may well be incidental in relation to the overall goals of the subject, since technical rigor and the use of mathematical methods seem to be essential in all areas of logical research.
However, the distinction between the two subfields has been magnified by differences in the sorts of professional training that are available to logicians, and by the views of individuals on what is important for the field.
The statement of policy presented in Journal of Symbolic Logicdissertation on artificial intelligence regarding robots, Volume 1, No.
Probably at this time both the mathematicians and the philosophers shared a sense that their subject was considered to be somewhat marginal by their colleagues, and may have felt a primary loyalty to logic as a subject rather than to any academic discipline. Articles in the first volume of the JSL were divided about equally between professional mathematicians and philosophers, and the early volumes of the JSL do not show any strong differences between the two groups as to topic.
This situation changed in the s. The volume of the JSL contained 39 articles by mathematicians, and only nine by philosophers. By the early s, many philosophers felt that philosophical papers on logic were unlikely to be accepted by the JSLand that if they were accepted they were unlikely to be read by philosophers.
At this point, the goals of the two groups had diverged considerably. Mathematicians were pursuing the development of an increasingly technical and complex body of methods and theorems. Many philosophers felt that this pursuit was increasingly irrelevant to the goal of illuminating philosophical issues. These divisions led to the founding of the Journal of Philosophical Logic in The list of sample topics in the first issue included:. Most of the articles over the subsequent 28 years of the JPL belong to the first of these four categories.
But the description with which this list begins is not particularly illuminating: why should these particular topics be of interest to philosophers? Their most important shared feature is a sense that despite successes in formalizing areas of mathematical logic, the scope of dissertation on artificial intelligence regarding robots remained severely limited.
There are unsolved problems in formalizing the nonmathematical sciences that seem to require thinking through new and different logical issues quantum logic and the logic of induction, for instance.
The remaining topics cover a part, at least, of the even more pressing dissertation on artificial intelligence regarding robots involved in extending logical theory to nonscientific reasoning. The dominant goal, then, of philosophical logic dissertation on artificial intelligence regarding robots the extension of logical methods to nonmathematical reasoning domains. This goal has a theoretical dimension if as many philosophical logicians seem to feel it requires reworking and extending logical formalisms.
The development and testing of applications, such as the problem of formalizing the reasoning involved in getting to the airport, posed as a challenge in McCarthy see Section 2. Essentially, this means that the theories are motivated and tested with small-scale, dissertation on artificial intelligence regarding robots, artificial examples, selected by the theoreticians. These examples usually serve more as demonstrations or illustrations than as tests.
The rough comparison in Section 1. Theoretical work in logical AI and in philosophical logic overlap to a large extent. Both are interested in developing nonmetamathematical applications of logic, and the core topics are very similar. This overlap is due not only to commonality of interest, but to direct influence of philosophical logic on logical AI; there is ample evidence, as we will see, that the first generation at least of AI logicists read and were influenced by the literature in philosophical logic.
Since that point, the specialties have diverged. Dissertation on artificial intelligence regarding robots logical theories have emerged in logical AI nonmonotonic logic is the most important example which are not widely known in philosophical logic. Some have to do with the emerging development in computer science of ambitious applications using unprecedentedly large bodies of logical axioms.
The sheer size of these applications produces new problems and new methodologies. And other differences originate in the interest of philosophical logicians in some topics metaphysical topics, for instance that are primarily inspired by purely philosophical considerations. Concern for applications can be a great influence on how research is carried out and presented. The tradition in philosophical logic predates applications in automated reasoning, and to this day remains relatively uninterested in such applications.
The methodology depends on intuitions, dissertation on artificial intelligence regarding robots, but without any generally accepted methodology for articulating and deploying these intuitions.
And the ideas are illustrated and informed by artificial, small-scale examples. And it is hard to find cases in which the philosophical theories are illustrated or tested with realistic, large-scale reasoning problems.
These differences, however, are much more a matter of style than of substance or of strategic research goals, dissertation on artificial intelligence regarding robots. It is difficult to think through the details of the reasoning process without the computational tools to make the process concrete, and difficult to develop large-scale formalizations of reasoning problems without computational tools for entering, testing, and maintaining the formalizations.
Because the core theoretical topics modal, conditional and temporal logic, belief revision, and the logic of context are so similar, and because the ultimate goal the formalization of nonmathematical reasoning is the same, one can see logic in AI as a continuous extension of the philosophical logic tradition.
The early influence of philosophical logic on logic in AI was profound. There are 58 citations in the bibliography. Of these, 35 refer to the philosophical logic literature.
There are 17 computer science citations, one mathematical logic citation, one economics citation, and one psychology citation. This paper was written at a time when there were hardly any references to logical AI in the computer science literature. Naturally, as logical AI has matured and developed as a branch of computer science, the proportion of cross-disciplinary citations has decreased. A sampling of articles from the first Knowledge Representation conference, Brachman et al, dissertation on artificial intelligence regarding robots.
Despite the dramatic decrease in quantity of explicit citations, the contemporary literature in logical AI reflects an indirect acquaintance with the earlier literature in philosophical logic, since many of the computational papers that are explicitly cited in the modern works were influenced by this literature. Of course, the influence becomes increasingly distant as time passes, and this trend is accelerated by the fact that new theoretical topics have been invented in logical AI that were at best only dimly prefigured in the philosophical literature.
Although philosophical logic is now a relatively small field in comparison to logical AI, it remains a viable area of research, with new work appearing regularly.
Artificial Intelligence - Research and Which Majors to Pick
, time: 12:06Logic and Artificial Intelligence (Stanford Encyclopedia of Philosophy)
Dissertation/Candidate Prerequisite: Graduate School authorization for admission as a doctoral candidate. (8 credits); (4 credits) Election for dissertation work by a doctoral student who has been admitted to candidate status. The defense of the dissertation, that is, the final oral examination, must be held under a full-term candidacy enrollment Jul 12, · Artificial intelligence (AI) is the field devoted to building artificial animals (or at least artificial creatures that – in suitable contexts – appear to be animals) and, for many, artificial persons (or at least artificial creatures that – in suitable contexts – appear to be persons). [] Such goals immediately ensure that AI is a discipline of considerable interest to many Jun 04, · Artificial Intelligence (AI) technologies are gaining considerable attention because of their rapid response speeds and robust capacity for generalization (Evans, ). Machine learning demonstrates good potential for assisting and enhancing traditional reservoir engineering approaches in a wide range of reservoir engineering issues(Anifowose
No comments:
Post a Comment