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v Computing
with Words, Computational Theory of Perceptions, Precisiated Natural
Language, and Perception-Based
Decision Analysis
The fuzzy logic
representation model for "perception-based information Processing: The
computational theory of perceptions suggests a new direction in AI--a
direction that may enhance the ability of AI to deal with real-world problems
in which decision-relevant information is a mixture of measurements and
perceptions. What is not widely recognized is that many important problems in
AI fall into this category. The base for CTP is the methodology of computing
with words (CW). In CW, the objects of computation are words and propositions
drawn from a natural language. The point of departure in the computational
theory of perceptions is the assumption that perceptions are described as
propositions in a natural language. Furthermore, computing and reasoning with
perceptions is reduced to computing and reasoning with words. Prof. Lotfi A.
Zadeh and Dr. Masoud Nikravesh are working
on CTP as part of BISC/CW-CTP-PNL-PDA Initiative. See http://www-bisc.cs.berkeley.edu
for more information.
v
The BISC Decision Support System
There There is a need for
an initiative to develop intelligent
real-time automated decision-making and management system based on two main
motivations:
·
In recent years, a
decline in revenue, needs for more cost effective strategy and multicriteria
and multiattribute optimization in an imprecise and uncertain environment have
emphasized the need for risk and uncertainty management in the distributed
and complex dynamic systems.
·
There exists an
ever-increasing need to improve technology that provides a global solution
to modeling, understanding, analyzing and managing imprecision and risk in
real-time automated decision-making for complex distributed dynamic
systems.
v Autonomous
Multi-Agent System with Perception-Based Reinforcement Learning Capabilities
A key component of any autonomous
multi-agent system--especially in an adversarial setting--is the decision
module, which should be capable of functioning in an environment of
imprecision, uncertainty, and imperfect reliability. This project will be
focused on the development of such a system and can be used as a
decision-support system for ranking of decision alternatives and can be used:
- As a global optimizer for planning decisions in
a distributed environment
- To facilitate the solution of complex problems
by a group of autonomous agents by speeding up the process of decision
making, collaboration, and sharing the information, goals, and
objectives
- To intelligently allocate resources given the
degree of match between objectives and resources available
- To assist autonomous multi-agent systems in
assessing the consequences of a decision made in an environment of
imprecision, uncertainty, and partial truth, and providing a systematic
risk analysis
- To assist multi-agent systems in answering
"what if" questions, examining numerous alternatives very
quickly, ranking of decision alternatives, and finding the value of the
inputs to achieve a desired level of output
v Fuzzy Logic
and the Internet: Perception-Based Information Processing and Retrieval
Intelligent information and knowledge retrieval
through Web-connectivity-based clustering: The web environment is, for
the most part, unstructured and imprecise. To deal with information in the
web environment what is needed is a logic that supports modes of reasoning
which are approximate rather than exact. While searches may retrieve
thousands of hits, finding decision-relevant and query-relevant information
in an imprecise environment is a challenging problem, which has to be
addressed. Another, and less obvious, is deduction in an unstructured and
imprecise environment given the huge stream of complex information. The objective of this initiative is to develop an intelligent computer
system with deductive capabilities to conceptually
cluster, match and rank pages based on predefined linguistic formulations and rules defined by experts
or based on a set of known homepages.
The Conceptual Fuzzy Set (CFS) model will be used for intelligent
information and knowledge retrieval through conceptual matching of both text
and images (here defined as “Concept”). The selected query doesn’t need to
match the decision criteria exactly, which gives the system a more human-like
behavior. The CFS can also be used for constructing fuzzy ontology or terms
related to the context of search or query to resolve the ambiguity.
v Artificial
DNA for Knowledge Discovery
The
uses of "biological DNA" to develop fuzzy-artificial-DNA model for
knowledge discovery and optimization: Motivated by current
advances in DNA computing which has been showed promises toward solving
complex problems including
"NP-complete" problems such as Hamiltonian path problem and
Satisfiability Problem with ability to pursue an unbounded number of
independent computational searches in parallel, we will use a new coding
method based on biological DNA and Artificial DNA computing to solve the optimization problem. The DNA coding
method and the mechanism of development from artificial DNA are suitable for
knowledge extraction including fuzzy IF ...THEN from large data set for Data
Mining purposes. We claim that Fuzzy- artificial DNA can be used for robust
optimization along the multidimensional, highly nonlinear and non-convex search
hyper-surfaces, generalize its estimation through evolution and manage the
uncertainty through fuzzy based technique, even though the environment may
partially observable.
v
Intelligent Reservoir Modeling for Optimized
Asset Management & Decision Making
There
is a need for an initiative in reservoir modeling and management based on two
main motivations:
·
A recent decline in
recovery factors and reserve replacement ratios, combined with a volatile oil
market and a steady rise in world oil production which emphasize the need for
risk and uncertainty management in oil exploration and production.
·
An ever-increasing need
to improve necessary technology that provides an efficient solution to
modeling, understanding, analyzing and managing uncertainty and risk in oil
exploration and production. Such improvements are required in both the
Business related issues and Earth Sciences areas.
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Toward a
perception-based theory of probabilistic reasoning
with
imprecise probabilities
Journal of Statistical Planning and
Inference 105 (2002) 233–264
Lotfi A. Zadeh
PERCEPTION-BASED INTELLIGENT DECISION SYSTEMS
Lotfi A. Zadeh and Masoud Nikravesh
(First Lotfi’s Powerpoint Presentation)
Reinventing
California’s Economy
Masoud Nikravesh
Intelligent
Information Management
Ben Azvine, Nader Azarmi, and
Masoud Nikravesh
Intelligent
Reservoir Characterization
Masoud Nikravesh
Soft Computing for
Reservoir Characterization
Masoud Nikravesh, Lotfi A. Zadeh,
Fred Aminzadeh
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