In this paper, we continue to explore many-valued disjunctive logic programs with probabilistic semantics. In particular, we newly introduce the least model state semantics for such programs. We show that many-valued disjunctive logic programs under the semantics of minimal models, perfect models, stable models, and least model states can be unfolded to equivalent classical disjunctive logic pr...

This paper presents the concept of the disjunctive bottom set and discusses its computation. The disjunctive bottom set differs from existing extensions of the bottom set, such as kernel sets(Ray, Broda, & Russo 2003), by being the weakest minimal single hypothesis for the whole hypothesis space. The disjunctive bottom set may be characterized in terms of minimal models. Therefore, as minimal m...

While the stable model semantics, in the form of Answer Set Programming, has become a successful semantics for disjunctive logic programs, a corresponding satisfactory extension of the well-founded semantics to disjunctive programs remains to be found. The many current proposals for such an extension are so diverse, that even a systematic comparison between them is a challenging task. In order ...

In this report, we provide the first characterization of abducens nucleus neuron (ABN) discharge dynamics during horizontal disjunctive saccades. These movements function to rapidly transfer the visual axes between targets located at different eccentricities and depths. Our primary objective was to determine whether the signals carried by ABNs during these movements are appropriate to drive the...

Disjunctive Answer Set Solvers Remi Brochenin1 and Yuliya Lierler2 and Marco Maratea3 Abstract. A fundamental task in answer set programming is to compute answer sets of logic programs. Answer set solvers are the programs that perform this task. The problem of deciding whether a disjunctive program has an answer set is Σ2 -complete. The high complexity of reasoning within disjunctive logic prog...