Partially-Distributed Coordination with Reo (Technical Report)

Coordination languages, as Reo, have emerged for the specification and implementation of interaction protocols among concurrent entities. In this paper, we propose a framework for generating partially-distributed, partially-centralized implementations of Reo connectors to improve 1) build-time compilation and 2) run-time throughput and parallelism. Our framework relies on the definition of a new formal product operator on constraint automata (Reo's formal semantics), which enables the formally correct distribution of disjoint parts of a coordination scheme over different machines according to several possible motivations (e.g., performance, privacy, QoS constraints, resource availability, network topology). First, we describe the design and a proof-of-concept implementation of our framework. Then, in a case study, we show and explain how a generated connector implementation can be executed in the Cloud and supports Big Data coordination

Partially distributed coordination with Reo and constraint automata

Algorithms and the Foundations of Software technolog

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for cooperative sequential decision making under uncertainty and MAs allow temporally extended and asynchronous action execution. To date, most methods assume the underlying Dec-POMDP model is known a priori or a full simulator is available during planning time. Previous methods which aim to address these issues suffer from local optimality and sensitivity to initial conditions. Additionally, few hardware demonstrations involving a large team of heterogeneous robots and with long planning horizons exist. This work addresses these gaps by proposing an iterative sampling based Expectation-Maximization algorithm (iSEM) to learn polices using only trajectory data containing observations, MAs, and rewards. Our experiments show the algorithm is able to achieve better solution quality than the state-of-the-art learning-based methods. We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.Comment: Accepted to the 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017

MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, `or distributed resource allocation. Solving such problems efiectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005

Alpha-conotoxin ImI disrupts central control of swimming in the medicinal leech

Medicinal leeches (Hirudo spp.) swim using a metachronal, front-to-back undulation. The behavior is generated by central pattern generators (CPGs) distributed along the animal’s midbody ganglia and is coordinated by both central and peripheral mechanisms. Here we report that a component of the venom of Conus imperialis, α-conotoxin ImI, known to block nicotinic acetyl-choline receptors in other species, disrupts swimming. Leeches injected with the toxin swam in circles with exaggerated dorsoventral bends and reduced forward velocity. Fictive swimming in isolated nerve cords was even more strongly disrupted, indicating that the toxin targets the CPGs and central coordination, while peripheral coordination partially rescues the behavior in intact animals

An Approach of Decision-Making Support Based on Collaborative Agents for a Large Distribution Sector

International audienceThis paper applies the multi-agent systems paradigm to collaborative coordination and negotiation in a global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders for which the quantity of product cannot be delivered partially or completely from the available inventory. This type of orders can be generated by unexpected swings in demand or unexpected exceptions (problem of production, problem of transportation, etc.). This paper proposes a first architecture and discusses an industrial case study

Discrete synchronization of hybrid systems

Control theory is currently faced with new paradigms and challenges that fall beyond traditional problems. Nowadays applications tend to be distributed, and require partial synchronization among their various subsystems. In this paper, we give initial steps towards discrete synchronization problems for systems which are compositions of several, possibly distributed, hybrid systems. Such problems arise frequently in the coordination of multi-agent systems, where each agent is modeled as a hybrid system. This results in control problems where the model is the composition of decoupled subsystems, but the specification is coupled across subsystems. A centralized solution to this problem requires computing the product hybrid systems resulting in state explosion. We alternatively consider decentralized solutions to such discrete synchronization problems. Partially decentralized synchronization is achieved if each subsystem is allowed to communicate with the subsystems it needs to partially synchronize with. The required communication between agents is provided by mobile abstractions of the remaining agents. These abstractions, which are property-dependent, are then used to derive local controllers using global, but minimal, observations