304,028 research outputs found
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes:
either on small improvements in narrow AI domains, or on universal theoretical
frameworks which are usually uncomputable, incompatible with theories of
biological intelligence, or lack practical implementations. The goal of this
work is to combine the main advantages of the two: to follow a big picture
view, while providing a particular theory and its implementation. In contrast
with purely theoretical approaches, the resulting architecture should be usable
in realistic settings, but also form the core of a framework containing all the
basic mechanisms, into which it should be easier to integrate additional
required functionality.
In this paper, we present a novel, purposely simple, and interpretable
hierarchical architecture which combines multiple different mechanisms into one
system: unsupervised learning of a model of the world, learning the influence
of one's own actions on the world, model-based reinforcement learning,
hierarchical planning and plan execution, and symbolic/sub-symbolic integration
in general. The learned model is stored in the form of hierarchical
representations with the following properties: 1) they are increasingly more
abstract, but can retain details when needed, and 2) they are easy to
manipulate in their local and symbolic-like form, thus also allowing one to
observe the learning process at each level of abstraction. On all levels of the
system, the representation of the data can be interpreted in both a symbolic
and a sub-symbolic manner. This enables the architecture to learn efficiently
using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl
Translating expert system rules into Ada code with validation and verification
The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system
Specifications and Development of Interoperability Solution dedicated to Multiple Expertise Collaboration in a Design Framework
This paper describes the specifications of an interoperability platform based on the PPO (Product Process Organization) model developed by the French community IPPOP in the context of collaborative and innovative design. By using PPO model as a reference, this work aims to connect together heterogonous tools used by experts easing data and information exchanges. After underlining the growing needs of collaborative design process, this paper focuses on interoperability concept by describing current solutions and their limits. Then a solution based on the flexibility of the PPO model adapted to the philosophy of interoperability is proposed. To illustrate these concepts, several examples are more particularly described (robustness analysis, CAD and Product Lifecycle Management systems connections)
A knowledge representation meta-model for rule-based modelling of signalling networks
The study of cellular signalling pathways and their deregulation in disease
states, such as cancer, is a large and extremely complex task. Indeed, these
systems involve many parts and processes but are studied piecewise and their
literatures and data are consequently fragmented, distributed and sometimes--at
least apparently--inconsistent. This makes it extremely difficult to build
significant explanatory models with the result that effects in these systems
that are brought about by many interacting factors are poorly understood.
The rule-based approach to modelling has shown some promise for the
representation of the highly combinatorial systems typically found in
signalling where many of the proteins are composed of multiple binding domains,
capable of simultaneous interactions, and/or peptide motifs controlled by
post-translational modifications. However, the rule-based approach requires
highly detailed information about the precise conditions for each and every
interaction which is rarely available from any one single source. Rather, these
conditions must be painstakingly inferred and curated, by hand, from
information contained in many papers--each of which contains only part of the
story.
In this paper, we introduce a graph-based meta-model, attuned to the
representation of cellular signalling networks, which aims to ease this massive
cognitive burden on the rule-based curation process. This meta-model is a
generalization of that used by Kappa and BNGL which allows for the flexible
representation of knowledge at various levels of granularity. In particular, it
allows us to deal with information which has either too little, or too much,
detail with respect to the strict rule-based meta-model. Our approach provides
a basis for the gradual aggregation of fragmented biological knowledge
extracted from the literature into an instance of the meta-model from which we
can define an automated translation into executable Kappa programs.Comment: In Proceedings DCM 2015, arXiv:1603.0053
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