2 research outputs found
A Modelling Approach Based on Fuzzy Agents
Modelling of complex systems is mainly based on the decomposition of these
systems in autonomous elements, and the identification and definitio9n of
possible interactions between these elements. For this, the agent-based
approach is a modelling solution often proposed. Complexity can also be due to
external events or internal to systems, whose main characteristics are
uncertainty, imprecision, or whose perception is subjective (i.e. interpreted).
Insofar as fuzzy logic provides a solution for modelling uncertainty, the
concept of fuzzy agent can model both the complexity and uncertainty. This
paper focuses on introducing the concept of fuzzy agent: a classical
architecture of agent is redefined according to a fuzzy perspective. A
pedagogical illustration of fuzzy agentification of a smart watering system is
then proposed.Comment: 10 pages, 8 figures, 35 reference
Extending Prolog with Incomplete Fuzzy Information
Incomplete information is a problem in many aspects of actual environments.
Furthermore, in many sceneries the knowledge is not represented in a crisp way.
It is common to find fuzzy concepts or problems with some level of uncertainty.
There are not many practical systems which handle fuzziness and uncertainty and
the few examples that we can find are used by a minority. To extend a popular
system (which many programmers are using) with the ability of combining crisp
and fuzzy knowledge representations seems to be an interesting issue.
Our first work (Fuzzy Prolog) was a language that models
-valued Fuzzy Logic. In the Borel algebra,
, truth value is represented using unions of intervals of
real numbers. This work was more general in truth value representation and
propagation than previous works.
An interpreter for this language using Constraint Logic Programming over Real
numbers (CLP()) was implemented and is available in the Ciao system.
Now, we enhance our former approach by using default knowledge to represent
incomplete information in Logic Programming. We also provide the implementation
of this new framework. This new release of Fuzzy Prolog handles incomplete
information, it has a complete semantics (the previous one was incomplete as
Prolog) and moreover it is able to combine crisp and fuzzy logic in Prolog
programs. Therefore, new Fuzzy Prolog is more expressive to represent real
world.
Fuzzy Prolog inherited from Prolog its incompleteness. The incorporation of
default reasoning to Fuzzy Prolog removes this problem and requires a richer
semantics which we discuss