Inductive learning searches an optimal hypothesis that minimizes a given loss function. It is
usually assumed that the simplest hypothesis that fits the data is the best approximate to an …

This paper presents distributed self-stabilizing algorithms for the maximal independent and
the minimal dominating set problems. Using an unfair distributed scheduler the algorithms …

We propose a general self-stabilizing scheme for solving any synchronization problem
whose safety specification can be defined using a local property. We demonstrate the …

We survey existing scheduling hypotheses made in the literature in self-stabilization,
commonly referred to under the notion of daemon. We show that four main characteristics …

A self-stabilizing algorithm, regardless of the initial system state, converges in nite time to a
set of states that satisfy a legitimacy predicate without the need for explicit exception handler …

We propose two distributed algorithms to maintain, respectively, a maximal matching and a
maximal independent set in a given ad hoc network; our algorithms are fault tolerant …

Self-stabilization is a theoretical framework of nonmasking fault-tolerant distributed
algorithms. In this paper, we propose a self-stabilizing algorithm for the maximal …

In the state-based self-stabilizing algorithmic paradigm for distributed computing, each node
has only a local view of the system (seeing its neighbors' states), yet in a finite amount of …

In this paper we propose a new self-stabilizing distributed algorithm for minimal domination
protocol in an arbitrary network graph using the synchronous model; the proposed protocol …

We propose two new self-stabilizing distributed algorithms for proper Δ+ 1 (Δ is the
maximum degree of a node in the graph) colorings of arbitrary system graphs. Both …