282 research outputs found
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
Complexity of fuzzy answer set programming under Łukasiewicz semantics
Fuzzy answer set programming (FASP) is a generalization of answer set programming (ASP) in which propositions are allowed to be graded. Little is known about the computational complexity of FASP and almost no techniques are available to compute the answer sets of a FASP program. In this paper, we analyze the computational complexity of FASP under Łukasiewicz semantics. In particular we show that the complexity of the main reasoning tasks is located at the first level of the polynomial hierarchy, even for disjunctive FASP programs for which reasoning is classically located at the second level. Moreover, we show a reduction from reasoning with such FASP programs to bilevel linear programming, thus opening the door to practical applications. For definite FASP programs we can show P-membership. Surprisingly, when allowing disjunctions to occur in the body of rules – a syntactic generalization which does not affect the expressivity of ASP in the classical case – the picture changes drastically. In particular, reasoning tasks are then located at the second level of the polynomial hierarchy, while for simple FASP programs, we can only show that the unique answer set can be found in pseudo-polynomial time. Moreover, the connection to an existing open problem about integer equations suggests that the problem of fully characterizing the complexity of FASP in this more general setting is not likely to have an easy solution
A Potpourri of Reason Maintenance Methods
We present novel methods to compute changes to materialized
views in logic databases like those used by rule-based reasoners.
Such reasoners have to address the problem of changing axioms in the
presence of materializations of derived atoms. Existing approaches have
drawbacks: some require to generate and evaluate large transformed programs
that are in Datalog - while the source program is in Datalog and
significantly smaller; some recompute the whole extension of a predicate
even if only a small part of this extension is affected by the change.
The methods presented in this article overcome these drawbacks and derive
additional information useful also for explanation, at the price of an
adaptation of the semi-naive forward chaining
Aggregated fuzzy answer set programming
Fuzzy Answer Set programming (FASP) is an extension of answer set programming (ASP), based on fuzzy logic. It allows to encode continuous optimization problems in the same concise manner as ASP allows to model combinatorial problems. As a result of its inherent continuity, rules in FASP may be satisfied or violated to certain degrees. Rather than insisting that all rules are fully satisfied, we may only require that they are satisfied partially, to the best extent possible. However, most approaches that feature partial rule satisfaction limit themselves to attaching predefined weights to rules, which is not sufficiently flexible for most real-life applications. In this paper, we develop an alternative, based on aggregator functions that specify which (combination of) rules are most important to satisfy. We extend upon previous work by allowing aggregator expressions to define partially ordered preferences, and by the use of a fixpoint semantics
Solving Stochastic B\"uchi Games on Infinite Arenas with a Finite Attractor
We consider games played on an infinite probabilistic arena where the first
player aims at satisfying generalized B\"uchi objectives almost surely, i.e.,
with probability one. We provide a fixpoint characterization of the winning
sets and associated winning strategies in the case where the arena satisfies
the finite-attractor property. From this we directly deduce the decidability of
these games on probabilistic lossy channel systems.Comment: In Proceedings QAPL 2013, arXiv:1306.241
Multiobjective programming for type-2 hierarchical fuzzy inference trees
This paper proposes a design of hierarchical fuzzy inference tree (HFIT). An HFIT produces an
optimum tree-like structure. Specifically, a natural hierarchical structure that accommodates simplicity by
combining several low-dimensional fuzzy inference systems (FISs). Such a natural hierarchical structure
provides a high degree of approximation accuracy. The construction of HFIT takes place in two phases.
Firstly, a nondominated sorting based multiobjective genetic programming (MOGP) is applied to obtain a
simple tree structure (low model’s complexity) with a high accuracy. Secondly, the differential evolution
algorithm is applied to optimize the obtained tree’s parameters. In the obtained tree, each node has a
different input’s combination, where the evolutionary process governs the input’s combination. Hence,
HFIT nodes are heterogeneous in nature, which leads to a high diversity among the rules generated
by the HFIT. Additionally, the HFIT provides an automatic feature selection because it uses MOGP
for the tree’s structural optimization that accept inputs only relevant to the knowledge contained in
data. The HFIT was studied in the context of both type-1 and type-2 FISs, and its performance was
evaluated through six application problems. Moreover, the proposed multiobjective HFIT was compared
both theoretically and empirically with recently proposed FISs methods from the literature, such as
McIT2FIS, TSCIT2FNN, SIT2FNN, RIT2FNS-WB, eT2FIS, MRIT2NFS, IT2FNN-SVR, etc. From the
obtained results, it was found that the HFIT provided less complex and highly accurate models compared
to the models produced by most of the other methods. Hence, the proposed HFIT is an efficient and
competitive alternative to the other FISs for function approximation and feature selectio
A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees
FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language")
is a fuzzy logic programming language with implicit/explicit truth degree
annotations, a great variety of connectives and unification by similarity.
FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic
Programming, a fuzzy logic language with explicitly annotated rules) and
Bousi~Prolog (which uses a weak unification algorithm and is well suited for
flexible query answering). Hence, it properly manages similarity and truth
degrees in a single framework combining the expressive benefits of both
languages. This paper presents the main features and implementations details of
FASILL. Along the paper we describe its syntax and operational semantics and we
give clues of the implementation of the lattice module and the similarity
module, two of the main building blocks of the new programming environment
which enriches the FLOPER system developed in our research group.Comment: In Proceedings PROLE 2014, arXiv:1501.0169
- …