25,323 research outputs found
Probabilistic Dynamic Logic of Phenomena and Cognition
The purpose of this paper is to develop further the main concepts of
Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in
the previous paper. The specific character of these logics is in matching
vagueness or fuzziness of similarity measures to the uncertainty of models.
These logics are based on the following fundamental notions: generality
relation, uncertainty relation, simplicity relation, similarity maximization
problem with empirical content and enhancement (learning) operator. We develop
these notions in terms of logic and probability and developed a Probabilistic
Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of
probabilistic models of brain. In our research the effectiveness of suggested
formalization is demonstrated by approximation of the expert model of breast
cancer diagnostic decisions. The P-DL-PC logic was previously successfully
applied to solving many practical tasks and also for modelling of some
cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence
July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number:
CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336
Artificial Intellignce: Art or Science?
Computer programs are new kinds of machines with great potential for improving the quality of life. In particular, expert systems could improve the ability of the small, weak and poor members of society to access the information they need to solve their problems. However, like most areas of computing, expert systems design is currently practiced as an art. In order to realise its potential it must also become an engineering science: providing the kinds of assurances of reliability that are normal in other branches of engineering. The way to do this is to put the techniques used to build expert systems and other artificial intelligence programs onto a sound theoretical foundation. The tools of mathematical logic appear to be a good basis for doing this, but we need to be imaginative in their use-not restricting ourselves to the kind of deductive reasoning usually thought of as 'logical', but investigating other aspects of reasoning, including uncertain reasoning, making conjectures and the guidance of inference. Acknow ledgement
TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system
Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system
Time series forecasting using a TSK fuzzy system tuned with simulated annealing
In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction
Learning Task Specifications from Demonstrations
Real world applications often naturally decompose into several sub-tasks. In
many settings (e.g., robotics) demonstrations provide a natural way to specify
the sub-tasks. However, most methods for learning from demonstrations either do
not provide guarantees that the artifacts learned for the sub-tasks can be
safely recombined or limit the types of composition available. Motivated by
this deficit, we consider the problem of inferring Boolean non-Markovian
rewards (also known as logical trace properties or specifications) from
demonstrations provided by an agent operating in an uncertain, stochastic
environment. Crucially, specifications admit well-defined composition rules
that are typically easy to interpret. In this paper, we formulate the
specification inference task as a maximum a posteriori (MAP) probability
inference problem, apply the principle of maximum entropy to derive an analytic
demonstration likelihood model and give an efficient approach to search for the
most likely specification in a large candidate pool of specifications. In our
experiments, we demonstrate how learning specifications can help avoid common
problems that often arise due to ad-hoc reward composition.Comment: NIPS 201
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