Reducing fuzzy answer set programming to model finding in fuzzy logics

In recent years, answer set programming (ASP) has been extended to deal with multivalued predicates. The resulting formalisms allow for the modeling of continuous problems as elegantly as ASP allows for the modeling of discrete problems, by combining the stable model semantics underlying ASP with fuzzy logics. However, contrary to the case of classical ASP where many efficient solvers have been constructed, to date there is no efficient fuzzy ASP solver. A well-known technique for classical ASP consists of translating an ASP program P to a propositional theory whose models exactly correspond to the answer sets of P. In this paper, we show how this idea can be extended to fuzzy ASP, paving the way to implement efficient fuzzy ASP solvers that can take advantage of existing fuzzy logic reasoners

Formal Specifications of Geographic Data Processing Requirements

This paper establishes a formal foundation for the specification of Geographic Data Processing (GDP) requirements. The emphasis is placed on modeling data and knowledge requirements rather than processing needs. A subset of first order logic is proposed as the principal means for constructing formalizations of the GDP requirements in a manner that is independent of the data representation. Requirements executability is achieved by selecting a subset of logic compatible with the inference mechanisms available in Prolog. GDP significant concepts such as time, space and accuracy have been added to the formalization without losing Prolog implementabilty or separation of concerns. Rules of reasoning about time, space and accuracy (based on positional, temporal and fuzzy logic) may be compactly stated in a subset of second order predicate calculus and may be easily modified to meet the particular needs of specific application. Multiple views of the data and knowledge may coexist in the same formalization. The feasibility of the approach has been established with the aid of a tentative Prolog implementation of the formalism. The implementation also provides the means for graphical rendering of logical information on a high resolution color display

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors