• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
Separate-and-Conquer Rule Learning
Separate-and-Conquer Rule Learning

... rules starts with a rule whose body is always true. As long as its still covers negative examples the current rule is specialized by adding conditions to its body. Possible conditions are tests on the presence of certain values of various attributes. In order to move towards the goal of finding a ru ...
Intelligent Agent
Intelligent Agent

Philosophical Aspects in Pattern Recognition Research
Philosophical Aspects in Pattern Recognition Research

... (e.g., biology and physics) and, even more profoundly, to the philosophical investigation. Hence, it is not surprising that many constitutive documents as well as psychological considerations or biological concepts, includes also many philosophical assertions ([66, 114] spring to mind). The same Dar ...
Document
Document

... Our Working Definition of AI Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if: • they could extend what they do to a World Wide Web-sized amount of data, and • not make mistakes. ...
Deep Learning for Artificial General Intelligence
Deep Learning for Artificial General Intelligence

... The presentation is organized with a view towards the integration of additional abilities into deep learning architectures, including: planning; reasoning and logic; data efficient learning and one-shot learning; program induction; additional learning algorithms other than backpropagation; more soph ...
Cognitive Science: An Introduction to the Study of Mind
Cognitive Science: An Introduction to the Study of Mind

... special. The major ideas that motivate each perspective and the problems each attempts to solve are laid out. Following this, we present factual background information that we believe is important and describe the approach’s methodology. The bulk of each chapter is devoted to detailing the specific ...
Study on Selection of Intelligent Waterdrop Algorithm for
Study on Selection of Intelligent Waterdrop Algorithm for

Artificial Intelligence – Agents and Environments
Artificial Intelligence – Agents and Environments

... AI programming languages and NetLogo Several programming languages have been proposed over the years as being well suited to building computer systems for Artificial Intelligence. Historically, the most notable AI programming languages have been Lisp and Prolog. Lisp (and related dialects such as Co ...
Learning logical definitions from relations
Learning logical definitions from relations

Multi-Agent Systems Introduction
Multi-Agent Systems Introduction

... social, social able to take part to an organised activity, in order to achieve its goals, by interacting with action other agents and Interaction users users. ...
On the Implementation of MIPS
On the Implementation of MIPS

... very common in single state exploration to avoid duplicates in the search. Usually, the memory structure is realized as a hash table which in this context is referred to by the term transposition table. For symbolic search this technique is called forward set simplification. Let reached be the BDD r ...
integrating ai techniques in sdlc: requirements phase perspective
integrating ai techniques in sdlc: requirements phase perspective

... AI(E3):Keyword Mapping: Many system development failures occur because the stakeholders cannot describe their requirements correctly, or developers and domain experts neglect “observable” words that contribute basically to system requirements. These challenges can be avoided by mapping each keyword ...
Hardness-Aware Restart Policies
Hardness-Aware Restart Policies

... Gomes et al. [7] demonstrated the effectiveness of randomized restarts on a variety of problems in scheduling, theorem-proving, and planning. In this approach, randomness is added to the branching heuristic of a systematic search algorithm; if the search algorithm does not find a solution within a g ...
Supervised and unsupervised learning.
Supervised and unsupervised learning.

... Petr Pošík Czech Technical University in Prague Faculty of Electrical Engineering Dept. of Cybernetics This lecture is based on the book Ten Lectures on Statistical and Structural Pattern Recognition ...
Unifying Instance-Based and Rule
Unifying Instance-Based and Rule

... algorithms are prone to be cumbersome, and often achieve accuracies that lie between those of their parents, instead of matching the highest. Here a theoretical question arises. It is well known that no induction algorithm can be the best in all possible domains; each algorithm contains an explicit ...
AGAINST NARROW OPTIMIZATION AND SHORT HORIZONS: AN
AGAINST NARROW OPTIMIZATION AND SHORT HORIZONS: AN

... [Boehm-Hansen00], and even the mean-maximization subject to variance-limit portfolio methods [Lintner65]. Hundreds of articles appear in on project management with similar sensibilities. This paper is sympathetic to the motivations of these approaches, though not necessarily to the logic or mathemat ...
Knowledge-based Manufacturing Enterprise and Enterprise Knowledge Management
Knowledge-based Manufacturing Enterprise and Enterprise Knowledge Management

... Knowledge management technology(KMT) is computer-based information technology that can help people produce, store, process and transfer knowledge, which is built on data management and information management technology. KMT makes knowledge management personnel and knowledge workers produce, share, a ...
Pattern-Database Heuristics for Partially Observable
Pattern-Database Heuristics for Partially Observable

... Eff is a finite set of partial states eff , the nondeterministic outcomes of a. The application of a nondeterministic outcome eff to a state s is the state app(eff , s) that results from updating s with eff . The application of an effect Eff to s is the set of states app(Eff , s) = {app(eff , s) | e ...
LNCS 3258 - Full Dynamic Substitutability by SAT Encoding
LNCS 3258 - Full Dynamic Substitutability by SAT Encoding

... flipping all its occurrences in the problem. These transformations do not affect the solvability or intrinsic hardness of a problem, and can be used to find average behaviour of deterministic solvers. They are also used in solver competitions; for details on them see [10]. We applied them and took m ...
A comprehensive survey of multi
A comprehensive survey of multi

... in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A sig ...
A Low-Cost Approximate Minimal Hitting Set Algorithm
A Low-Cost Approximate Minimal Hitting Set Algorithm

... cost/completeness trade-off. To the best of knowledge this heuristic approach has not been presented before and has proven to have a significant effect on MBD complexity in practice (Abreu, Zoeteweij, and Van Gemund 2009). The reminder of this paper is organized as follows. We start by introducing t ...
Study guide
Study guide

Narrative Intelligence - Carnegie Mellon School of Computer Science
Narrative Intelligence - Carnegie Mellon School of Computer Science

...  Intentional State Entailment: When people are acting in a narrative, the important part is not what the people do, but how they think and feel about what they do.  Hermeneutic Composability: Just as a narrative comes to life from the actions of which it is composed, those actions are understood w ...
The Problem of Logical-Form Equivalence
The Problem of Logical-Form Equivalence

The Intelligent Conversational Humanoid Robot
The Intelligent Conversational Humanoid Robot

... “Can machines think?” This is the question asked by Alan Turing which has since spawned numerous, passionate debates on the subject of artificial intelligence [1]. It has also spawned the famous Turing Test, a test which determines if a particular machine (or algorithm) can pass as a human. Since it ...
< 1 ... 5 6 7 8 9 10 11 12 13 ... 241 >

History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report