9 research outputs found
Représentations graphiques et intelligence artificielle
Nous discuterons, dans cet article, des représentations
graphiques en intelligence artificielle. Comme pour d'autres domaines, les
représentations graphiques permettent l'expression, informelle, des
données, structures de programmes ou de systèmes. Nous
développerons toutefois plus avant ce qui est sans doute
spécifique de l'intelligence artificielle, à savoir le fait que
certains types de représentations graphiques sont
"théorisés" afin de pouvoir effectuer interprétations et
calculs. Réseaux sémantiques et graphes conceptuels serviront de
support à notre propos. Nous indiquerons enfin l'état actuel des
formalisations et les difficultés faisant l'objet de recherches
actuelles
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A Connectionist Encoding of Semantic Networks
Although the connectionist approach has lead to elegant solutions to a number of problems in cognitive science and artificial intelligence, its suitability for dealing with iHX)blems in knowledge representation and inference has often been questioned. This paper partially answers this criticism by demonstrating that effective solutions to certain problems in knowledge representation and limited inference can be found by adopting a connectionist approach.The paper iM-esents a connectionist realization of semantic networks, i.e. it describes h o w knowledge about concepts, their properties, and the hierarchical relationship between them may be encoded as an interpreter-free massively parallel network of simple processing elements that can solve an interesting class of inheritance and recognition problems extremely fast - in time proprotional to the depth of the conceptual hierarchy. The connectionist realization is based on an evidential formulation that leads to principled solutions to the problems of exceptions, multiple inheritance, and conflicting information during inheritance, and the best match or partial match computation during recognition
Digitally produced judgements in modern court proceedings
Effective Protection of Fundamental Rights in a pluralist worl
Massively parallel reasoning in transitive relationship hierarchies
This research focuses on building a parallel knowledge representation and reasoning system for the purpose of making progress in realizing human-like intelligence. To achieve human-like intelligence, it is necessary to model human reasoning processes by programs. Knowledge in the real world is huge in size, complex in structure, and is also constantly changing even in limited domains. Unfortunately, reasoning algorithms are very often intractable, which means that they are too slow for any practical applications. One technique to deal with this problem is to design special-purpose reasoners. Many past Al systems have worked rather nicely for limited problem sizes, but attempts to extend them to realistic subsets of world knowledge have led to difficulties. Even special purpose reasoners are not immune to this impasse. In this work, to overcome this problem, we are combining special purpose reasoners with massive
We have developed and implemented a massively parallel transitive closure reasoner, called Hydra, that can dynamically assimilate any transitive, binary relation and efficiently answer queries using the transitive closure of all those relations. Within certain limitations, we achieve constant-time responses for transitive closure queries. Hydra can dynamically insert new concepts or new links into a. knowledge base for realistic problem sizes. To get near human-like reasoning capabilities requires the possibility of dynamic updates of the transitive relation hierarchies. Our incremental, massively parallel, update algorithms can achieve almost constant time updates of large knowledge bases.
Hydra expands the boundaries of Knowledge Representation and Reasoning in a number of different directions: (1) Hydra improves the representational power of current systems. We have developed a set-based representation for class hierarchies that makes it easy to represent class hierarchies on arrays of processors. Furthermore, we have developed and implemented two methods for mapping this set-based representation onto the processor space of a Connection Machine. These two representations, the Grid Representation and the Double Strand Representation successively improve transitive closure reasoning in terms of speed and processor utilization. (2) Hydra allows fast rerieval and dynamic update of a large knowledge base. New fast update algorithms are formulated to dynamically insert new concepts or new relations into a knowledge base of thousands of nodes. (3) Hydra provides reasoning based on mixed hierarchical representations. We have designed representational tools and massively parallel reasoning algorithms to model reasoning in combined IS-A, Part-of, and Contained-in hierarchies. (4) Hydra\u27s reasoning facilities have been successfully applied to the Medical Entities Dictionary, a large medical vocabulary of Columbia Presbyterian Medical Center.
As a result of (1) - (3), Hydra is more general than many current special-purpose reasoners, faster than currently existing general-purpose reasoners, and its knowledge base can be updated dynamically
Formal concept matching and reinforcement learning in adaptive information retrieval
The superiority of the human brain in information retrieval (IR) tasks seems to come firstly
from its ability to read and understand the concepts, ideas or meanings central to documents, in
order to reason out the usefulness of documents to information needs, and secondly from its
ability to learn from experience and be adaptive to the environment. In this work we attempt to
incorporate these properties into the development of an IR model to improve document
retrieval. We investigate the applicability of concept lattices, which are based on the theory of
Formal Concept Analysis (FCA), to the representation of documents. This allows the use of
more elegant representation units, as opposed to keywords, in order to better capture
concepts/ideas expressed in natural language text. We also investigate the use of a
reinforcement leaming strategy to learn and improve document representations, based on the
information present in query statements and user relevance feedback. Features or concepts of
each document/query, formulated using FCA, are weighted separately with respect to the
documents they are in, and organised into separate concept lattices according to a subsumption
relation. Furthen-nore, each concept lattice is encoded in a two-layer neural network structure
known as a Bidirectional Associative Memory (BAM), for efficient manipulation of the
concepts in the lattice representation. This avoids implementation drawbacks faced by other
FCA-based approaches. Retrieval of a document for an information need is based on concept
matching between concept lattice representations of a document and a query. The learning
strategy works by making the similarity of relevant documents stronger and non-relevant
documents weaker for each query, depending on the relevance judgements of the users on
retrieved documents. Our approach is radically different to existing FCA-based approaches in
the following respects: concept formulation; weight assignment to object-attribute pairs; the
representation of each document in a separate concept lattice; and encoding concept lattices in
BAM structures. Furthermore, in contrast to the traditional relevance feedback mechanism, our
learning strategy makes use of relevance feedback information to enhance document
representations, thus making the document representations dynamic and adaptive to the user
interactions. The results obtained on the CISI, CACM and ASLIB Cranfield collections are
presented and compared with published results. In particular, the performance of the system is
shown to improve significantly as the system learns from experience.The School of Computing,
University of Plymouth, UK
From surfaces to objects : Recognizing objects using surface information and object models.
This thesis describes research on recognizing partially obscured objects using
surface information like Marr's 2D sketch ([MAR82]) and surface-based geometrical
object models. The goal of the recognition process is to produce a fully
instantiated object hypotheses, with either image evidence for each feature or
explanations for their absence, in terms of self or external occlusion.
The central point of the thesis is that using surface information should be
an important part of the image understanding process. This is because surfaces
are the features that directly link perception to the objects perceived (for
normal "camera-like" sensing) and because surfaces make explicit information
needed to understand and cope with some visual problems (e.g. obscured features).
Further, because surfaces are both the data and model primitive, detailed
recognition can be made both simpler and more complete.
Recognition input is a surface image, which represents surface orientation and
absolute depth. Segmentation criteria are proposed for forming surface patches
with constant curvature character, based on surface shape discontinuities which
become labeled segmentation- boundaries.
Partially obscured object surfaces are reconstructed using stronger surface based
constraints. Surfaces are grouped to form surface clusters, which are 3D
identity-independent solids that often correspond to model primitives. These are
used here as a context within which to select models and find all object features.
True three-dimensional properties of image boundaries, surfaces and surface
clusters are directly estimated using the surface data.
Models are invoked using a network formulation, where individual nodes
represent potential identities for image structures. The links between nodes are
defined by generic and structural relationships. They define indirect evidence relationships
for an identity. Direct evidence for the identities comes from the data
properties. A plausibility computation is defined according to the constraints inherent
in the evidence types. When a node acquires sufficient plausibility, the
model is invoked for the corresponding image structure.Objects are primarily represented using a surface-based geometrical model.
Assemblies are formed from subassemblies and surface primitives, which are
defined using surface shape and boundaries. Variable affixments between assemblies
allow flexibly connected objects.
The initial object reference frame is estimated from model-data surface relationships,
using correspondences suggested by invocation. With the reference
frame, back-facing, tangential, partially self-obscured, totally self-obscured and
fully visible image features are deduced. From these, the oriented model is used
for finding evidence for missing visible model features. IT no evidence is found,
the program attempts to find evidence to justify the features obscured by an unrelated
object. Structured objects are constructed using a hierarchical synthesis
process.
Fully completed hypotheses are verified using both existence and identity
constraints based on surface evidence.
Each of these processes is defined by its computational constraints and are
demonstrated on two test images. These test scenes are interesting because they
contain partially and fully obscured object features, a variety of surface and solid
types and flexibly connected objects. All modeled objects were fully identified
and analyzed to the level represented in their models and were also acceptably
spatially located.
Portions of this work have been reported elsewhere ([FIS83], [FIS85a], [FIS85b],
[FIS86]) by the author
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Using Extended Logic Programs to Formalize Commonsense Reasoning
In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose