Constraint-based protocols for distributed problem solving

AbstractDistributed Problem Solving (DPS) approaches decompose problems into subproblems to be solved by interacting, cooperative software agents. Thus, DPS is suitable for solving problems characterized by many interdependencies among subproblems in the context of parallel and distributed architectures. Concurrent Constraint Programming (CCP) provides a powerful execution framework for DPS where constraints define local problem solving and the exchange of information among agents declaratively. To optimize DPS, the protocol for constraint communication must be tuned to the specific kind of DPS problem and the characteristics of the underlying system architecture. In this paper, we provide a formal framework for modeling different problems and we show how the framework applies to simple yet generalizable examples

Graph coloring-based multichannel MAC protocol in distributed underwater acoustic sensor networks

In this paper, the multichannel medium access control (MAC) problem in distributed underwater acoustic sensor networks (UASNs) were investigated. Compared with single-channel MAC protocols in terrestrial radio networks, there exist multichannel hidden terminal problem and long-delay hidden terminal problem in multichannel MAC protocol due to long propagation delay in UASNs. In addition, energy constraint makes channel allocation a challenging problem in distributed UASNs. To solve these aforementioned problems, a new multichannel MAC protocol, called graph coloring-based multichannel MAC protocol (GCMAC) is present. The protocol GCMAC is a synchronized MAC protocol which splits the time into three phases, namely, channel negotiation phase, channel selecting phase and data transmission phase. Specially, the rule for selecting channel is carefully designed based on graph coloring theory to avoid collision and maximize the utilization rate of channels in channel selecting phase. Simulation results show that GCMAC can greatly improve the system throughput and energy efficiency by effectively solving the hidden terminal problems and channel allocation problem

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Liveness of Randomised Parameterised Systems under Arbitrary Schedulers (Technical Report)

We consider the problem of verifying liveness for systems with a finite, but unbounded, number of processes, commonly known as parameterised systems. Typical examples of such systems include distributed protocols (e.g. for the dining philosopher problem). Unlike the case of verifying safety, proving liveness is still considered extremely challenging, especially in the presence of randomness in the system. In this paper we consider liveness under arbitrary (including unfair) schedulers, which is often considered a desirable property in the literature of self-stabilising systems. We introduce an automatic method of proving liveness for randomised parameterised systems under arbitrary schedulers. Viewing liveness as a two-player reachability game (between Scheduler and Process), our method is a CEGAR approach that synthesises a progress relation for Process that can be symbolically represented as a finite-state automaton. The method is incremental and exploits both Angluin-style L*-learning and SAT-solvers. Our experiments show that our algorithm is able to prove liveness automatically for well-known randomised distributed protocols, including Lehmann-Rabin Randomised Dining Philosopher Protocol and randomised self-stabilising protocols (such as the Israeli-Jalfon Protocol). To the best of our knowledge, this is the first fully-automatic method that can prove liveness for randomised protocols.Comment: Full version of CAV'16 pape