6,270 research outputs found
AI at Ames: Artificial Intelligence research and application at NASA Ames Research Center, Moffett Field, California, February 1985
Charts are given that illustrate function versus domain for artificial intelligence (AI) applications and interests and research area versus project number for AI research. A list is given of project titles with associated project numbers and page numbers. Also, project descriptions, including title, participants, and status are given
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a âgraph providerâ in order to
transfer the load of computation to the best suited component â
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
âsourceâ or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
Plan Verification in a Programmer's Apprentice
This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under the Office of Naval Research contract N00014-75-C-0643.Brief Statement of the Problem:
An interactive programming environment called the Programmer's Apprentice is described. Intended for use by the expert programmer in the process of program design and maintenance, the apprentice will be capable of understanding, explaining and reasoning about the behavior of real-world LISP programs with side effects on complex data-structures. We view programs as engineered devices whose analysis must be carried out at many level of abstraction. This leads to a set of logical dependencies between modules which explains how and why modules interact to achieve an overall intention. Such a network of dependencies is a teleological structure which we call a plan; the process of elucidating such a plan stucture and showing that it is coherent and that it achieves its overall intended behavior we call plan verification.
This approach to program verification is sharply contrasted with the traditional Floyd-Hoare systems which overly restrict themselves to surface features of the programming language. More similar in philosophy is the evolving methodology of languages like CLU or ALPHARD which stress conceptual layering.MIT Artificial Intelligence Laboratory
Department of Defense Advanced Research Projects Agenc
Automatic differentiation in machine learning: a survey
Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in
machine learning. Automatic differentiation (AD), also called algorithmic
differentiation or simply "autodiff", is a family of techniques similar to but
more general than backpropagation for efficiently and accurately evaluating
derivatives of numeric functions expressed as computer programs. AD is a small
but established field with applications in areas including computational fluid
dynamics, atmospheric sciences, and engineering design optimization. Until very
recently, the fields of machine learning and AD have largely been unaware of
each other and, in some cases, have independently discovered each other's
results. Despite its relevance, general-purpose AD has been missing from the
machine learning toolbox, a situation slowly changing with its ongoing adoption
under the names "dynamic computational graphs" and "differentiable
programming". We survey the intersection of AD and machine learning, cover
applications where AD has direct relevance, and address the main implementation
techniques. By precisely defining the main differentiation techniques and their
interrelationships, we aim to bring clarity to the usage of the terms
"autodiff", "automatic differentiation", and "symbolic differentiation" as
these are encountered more and more in machine learning settings.Comment: 43 pages, 5 figure
A Taxonomy of Information Retrieval Models and Tools
Information retrieval is attracting significant attention due to the exponential growth of the amount of information available in digital format. The proliferation of information retrieval objects, including algorithms, methods, technologies, and tools, makes it difficult to assess their capabilities and features and to understand the relationships that exist among them. In addition, the terminology is often confusing and misleading, as different terms are used to denote the same, or similar, tasks.
This paper proposes a taxonomy of information retrieval models and tools and provides precise definitions for the key terms. The taxonomy consists of superimposing two views: a vertical taxonomy, that classifies IR models with respect to a set of basic features, and a horizontal taxonomy, which classifies IR systems and services with respect to the tasks they support.
The aim is to provide a framework for classifying existing information retrieval models and tools and a solid point to assess future developments in the field
A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI
The article identified 42 cognitive architectures for creating general
artificial intelligence (AGI) and proposed a set of interrelated functional
blocks that an agent approaching AGI in its capabilities should possess. Since
the required set of blocks is not found in any of the existing architectures,
the article proposes a new cognitive architecture for intelligent systems
approaching AGI in their capabilities. As one of the key solutions within the
framework of the architecture, a universal method of knowledge representation
is proposed, which allows combining various non-formalized, partially and fully
formalized methods of knowledge representation in a single knowledge base, such
as texts in natural languages, images, audio and video recordings, graphs,
algorithms, databases, neural networks, knowledge graphs, ontologies, frames,
essence-property-relation models, production systems, predicate calculus
models, conceptual models, and others. To combine and structure various
fragments of knowledge, archigraph models are used, constructed as a
development of annotated metagraphs. As components, the cognitive architecture
being developed includes machine consciousness, machine subconsciousness,
blocks of interaction with the external environment, a goal management block,
an emotional control system, a block of social interaction, a block of
reflection, an ethics block and a worldview block, a learning block, a
monitoring block, blocks of statement and solving problems, self-organization
and meta learning block
AI Lab Faculty
This document is meant to introduce new graduate students in the MIT AI Lab to the faculty members of the laboratory and their research interests. Each entry consists of the faculty member's picture, if available, some information on how to reach them, their responses to a few survey questions, and a few paragraphs excerpted from the AI Lab President's Report, as edited by Patrick Winston.MIT Artificial Intelligence Laborator
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