On the truck dispatching problem

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

The emergence of the Internet of things and the widespread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges, and possible future directions, we survey recent contributions to the field. We focus on robot agents and emphasize the challenges of MAS sub-fields including task decomposition, coalition formation, task allocation, perception, and multi-agent planning and control. While some components have seen more advancements than others, more research is required before effective autonomous MAS can be deployed in real smart city settings that are less restrictive than the assumed validation environments of MAS. Specifically, more autonomous end-to-end solutions need to be experimentally tested and developed while incorporating natural language ontology and dictionaries to automate complex task decomposition and leveraging big data advancements to improve perception algorithms for robotics.

Cooperative Heterogeneous Multi-Robot Systems: A Survey

The development of detailed, coherent, models of complex biological systems is recognized as a key requirement for integrating the increasing amount of experimental data. In addition, in-silico simulation of bio-chemical models provides an easy way to test different experimental conditions, helping in the discovery of the dynamics that regulate biological systems. However, the computational power required by these simulations often exceeds that available on common desktop computers and thus expensive high performance computing solutions are required. An emerging alternative is represented by general-purpose scientific computing on graphics processing units (GPGPU), which offers the power of a small computer cluster at a cost of approximately $400. Computing with a GPU requires the development of specific algorithms, since the programming paradigm substantially differs from traditional CPU-based computing. In this paper, we review some recent efforts in exploiting the processing power of GPUs for the simulation of biological systems.

/pdf/gpu-computing-for-systems-biology-3nah2a999o.pdf

GPU computing for systems biology

Modeling and simulation have long been dominated by equation-based approaches, until the recent advent of agent-based approaches. To curb the resulting complexity of models, Axelrod promoted the KISS principle: " Keep It Simple, Stupid ". But the community is divided and a new principle appeared: KIDS, " Keep It Descriptive Simple ". Richer models were thus developed for a variety of phenomena, while agent cognition still tends to be modelled with simple reactive particle-like agents. This is not always appropriate, in particular in the social sciences trying to account for the complexity of human behaviour. One solution is to model humans as BDI agents, an expressive paradigm using concepts from folk psychology, making it easier for modellers and users to understand the simulation. This paper provides a methodological guide to the use of BDI agents in social simulations, and an overview of existing methodologies and tools for using them.

https://hal.inria.fr/hal-01363962/document

BDI agents in social simulations: a survey

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.

/pdf/cooperative-multi-agent-planning-a-survey-1cou1poqb3.pdf

Cooperative Multi-Agent Planning: A Survey

A multi-agent VRP solver is presented in this paper. It utilizes the contract-net protocol based allocation and several improvement strategies. It provides the solution with the quality of 81% compared to the optimal solution on 115 benchmark instances in polynomial time. The self-organizing capability of the system successfully minimizes the number of vehicles used. The presented solver architecture supports great runtime parallelization with incremental increase of solution quality. The presented solver demonstrates applicability to the VRP problem and easy adaptation to problem variants.

https://www.ifaamas.org/Proceedings/aamas2010/pdf/01%20Full%20Papers/16_03_FP_0805.pdf

Agents towards vehicle routing problems

This contribution presents a distributed, multi-layer collision avoidance architecture supporting efficient utilization of air space shared by several autonomous aerial vehicles. Presented multi-layer architecture is based on deliberative deployment of several collision avoidance methods by the aircraft at the same time. Both cooperative and non-cooperative collision avoidance methods are presented in the paper. The robustness of the architecture is justified by means of experimental validation of multi-agent simulation

Agent-Based Multi-Layer Collision Avoidance to Unmanned Aerial Vehicles

Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. 
This paper reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.

/pdf/cooperative-multi-agent-planning-a-survey-oxq9822snf.pdf

Online planning algorithms are typically a tool of choice for dealing with sequential decision problems in combinatorial search spaces. Many such problems, however, also exhibit combinatorial actions, yet standard planning algorithms do not cope well with this type of "the curse of dimensionality." Following a recently opened line of related work on combinatorial multi-armed bandit (CMAB) problems, we propose a novel CMAB planning scheme, as well as two specific instances of this scheme, dedicated to exploiting what is called linear side information. Using a representative strategy game as a benchmark, we show that the resulting algorithms very favorably compete with the state-of-the-art.

Antonín Komenda

Papers

Cooperative Multi-Agent Planning: A Survey

Agents towards vehicle routing problems

Agent-Based Multi-Layer Collision Avoidance to Unmanned Aerial Vehicles

Cooperative Multi-Agent Planning: A Survey

On combinatorial actions and CMABs with linear side information