Piero P. Bonissone's Research Interests:
Soft Computing
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Timeline
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Papers
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(2010) [9] P. Bonissone, Soft Computing: A Continuously
Evolving Concept, Int. J.
Computational Intelligence Systems, to appear, 2010 - [GE GR Technical
Report, 2009GRC845, Sept. 2009 (pdf)]. Soft
Computing (SC) is a concept with constantly evolving semantics, as
researchers have adopted its main philosophy while adding various
interpretations and facets to this concept. Originally defined as a loose association or partnership of
components, SC has gone through several transformational phases. This paper will trace some of the phases
experienced by the author as part of his understanding of the evolution of SC
and its role in constructing decision-making models. The first phase is the hybridization phase,
driven by the inherit ease of integration of SC components. The second phase
is a two-level model characterization, based on the split between
object-level and meta-level reasoning. This phase, inspired by traditional AI
problem formulation, led to a third phase, in which we addressed the
knowledge and meta-knowledge representation required by each of these
reasoning levels using a linguistics analogy. The fourth phase is the
extension of the heuristics used at the meta-level, e.g., Metaheuristics (MH's) from evolutionary
MH's to other MH's methods. The fifth
and last phase, further described in this paper, is the proposal for a strong
separation between offline MH's (used for design and tuning) and online MH's
(used for models selection or aggregation.) This last view suggests a broader
use of SC components, since it enables us to use hybrid SC techniques at each
of the MH's levels as well as at the object level. Furthermore, this separation facilitates
the model lifecycle management, which is required to maintain the models
vitality and prevent their obsolescence over time.
(2007) [8] J. L. Verdegay, R. Yager, and P. Bonissone, On Heuristics as a Fundamental
Constituent of Soft Computing, Fuzzy Sets and Systems, 2008, vol.
159, no. 7, pp. 846-855 - [GE GR Technical Report, 2007GRC805, Although as
such one dates back the idea of setting the area of soft computing to 1990,
it was in 1994 that
(2004) [7] P. Bonissone, R. Subbu, N. Eklund, and T. Kiehl, Evolutionary Algorithms +
Domain Knowledge = Real-World Evolutionary Computation, IEEE
Transactions on Evolutionary Computation, 10(3): 256-280, June 2006 (pdf) - [GE GR Technical Report,
2004GRC142,
(2004) [6] P. Bonissone,
Soft Computing and Meta-heuristics: using knowledge and reasoning to
control search and vice-versa", Proc. SPIE Vol. 5200, Applications
and Science of Neural Networks, Fuzzy Systems and Evolutionary Computation V,
pp. 133-149, S. Diego, CA, Aug 2003 (pdf) - [GE GR Tech. Report, 2003GRC367, Meta-heuristics
are heuristic procedures used to tune, control, guide,
allocate computational resources or reason about object-level problem solvers
in order to improve their quality, performance, or efficiency. Offline
meta-heuristics define the best structural and/or parametric configurations
for the object-level model, while on-line heuristics generate run-time
corrections for the behavior of the same object-level solvers. Soft Computing
is a framework in which we encode domain knowledge to develop such metaheuristics. We explore the use of meta-heuristics in
three application areas: a) control; b) optimization; and c) classification.
In the context of control problems, we describe the use of evolutionary
algorithms to perform offline parametric tuning of fuzzy controllers, and the
use of fuzzy supervisory controllers to perform on-line mode-selection and
output interpolation. In the area of optimization, we illustrate the
application of fuzzy controllers to manage the transition from exploration to
exploitation of evolutionary algorithms that solve the optimization problem.
In the context of discrete classification problems, we have leveraged
evolutionary algorithms to tune knowledge-based classifiers and maximize
their coverage and accuracy.
(2001) [5] P. Bonissone
and K. Goebel, "Soft Computing for Diagnostics in Equipment
Service", Artificial Intelligence for Engineering Design, Analysis
and Manufacturing (AIEDAM), 15(4): 267-279, September 2001 (pdf)
- [GE GR Technical Report, 2001CRD037, March 2001(pdf)]. We present
methods and tools from the Soft Computing domain, which is used within the
diagnostics and prognostics framework to accommodate imprecision of real
systems. Soft Computing (SC) is an association of computing methodologies
that includes as its principal members fuzzy, neural, evolutionary, and
probabilistic computing. These methodologies enable us to deal with
imprecise, uncertain data and incomplete domain knowledge typically encountered
in real-world applications. We outline the advantages and disadvantages of
these methodologies and show how they can be combined to create synergistic
hybrid SC systems. We conclude the paper with a description of successful SC
case study applications to equipment diagnostics
(2000) [4] P. Bonissone,
Hybrid Soft Computing Systems: Where Are We Going?, in Proceedings of the 14th
European Conference on Artificial Intelligence (ECAI 2000), Soft
computing is an association of computing methodologies that includes fuzzy
logic, neuro-computing, evolutionary computing, and probabilistic computing.
After a brief overview of Soft Computing components, we will analyze some of
its most synergistic combinations. We will emphasize the development of smart
algorithm-controllers, such as the use of fuzzy logic to control the
parameters of evolutionary computing and, conversely, the application of
evolutionary algorithms to tune fuzzy controllers. We will focus on three
real-world applications of soft computing that leverage the synergism created
by hybrid systems
(1999) [3] P. Bonissone, Y-T Chen, K. Goebel, & P. Khedkar, Hybrid
Soft Computing Systems: Industrial and Commercial Applications, Proceedings
of the IEEE, 87(9): 1641-1667, September 1999, (pdf) Soft computing (SC) is an association of computing
methodologies that includes as its principal members
fuzzy logic, neurocomputing, evolutionary computing
and probabilistic computing. We present a collection of methods and tools
that can be used to perform diagnostics, estimation, and control. These tools
are a great match for real-world applications that are characterized by
imprecise, uncertain data and incomplete domain knowledge. We outline the
advantages of applying SC techniques and in particular the synergy derived from
the use of hybrid SC systems. We illustrate some combinations of hybrid SC
systems, such as fuzzy logic controllers (FLC's)
tuned by neural networks (NN's) and evolutionary
computing (EC), NN's tuned by EC or FLC's, and EC controlled by FLC's.
We discuss three successful real-world examples of SC applications to
industrial equipment diagnostics, freight train control, and residential
property valuation. (1998) [2] E.
Ruspini, P. Bonissone, and W. Pedrycz, Handbook
of Fuzzy Computing, Institute of Physics, Fall 1998, ISBN: 0750304278 (book)
(1997) [1] P. Bonissone, Soft Computing: the Convergence of Emerging Reasoning
Technologies, Journal of Research in Soft Computing,
Springer-Verlag, 1(1): 6-18, April 1997 The term Soft computing (SC) represents the
combination of emerging problem solving technologies such as Fuzzy Logic (FL)
Probabilistic Reasoning (PR), Neural Networks (NNs)
and Genetic Algorithms (GA). Each of
these technologies provides us with complementary reasoning and searching
methods fro solving complex, real-world problems. After a brief description
of each technology, we will analyze some of their most useful combinations,
such as the use of FL to control Gas and NNs
parameters; the application of Gas to evolve NNs
(topologies or weights)or to tune FL controllers; and the implementation of
FL controllers as NNs tuned by back-propagation
type algorithms. |
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