A Descriptive Model of Robot Team and the Dynamic Evolution of Robot Team Cooperation

At present, the research on robot team cooperation is still in qualitative analysis phase and lacks the description model that can quantitatively describe the dynamical evolution of team cooperative relationships with constantly changeable task demand in Multi-robot field. First this paper whole and static describes organization model HWROM of robot team, then uses Markov course and Bayesian theorem for reference, dynamical describes the team cooperative relationships building. Finally from cooperative entity layer, ability layer and relative layer we research team formation and cooperative mechanism, and discuss how to optimize relative action sets during the evolution. The dynamic evolution model of robot team and cooperative relationships between robot teams proposed and described in this paper can not only generalize the robot team as a whole, but also depict the dynamic evolving process quantitatively. Users can also make the prediction of the cooperative relationship and the action of the robot team encountering new demands based on this model. Journal web page & a lot of robotic related papers www.ars-journal.co

The Potential of an Enhanced Cooperation Measure in the EAFRD (2014-2020): the case of Ireland

This report was funded by the Department of Agriculture, Food and the Marine (DAFM) through the National Rural Network (February-May, 2012).The current Proposal for a Regulation of the European Parliament and of the Council on support for Rural Development by the European Agricultural Fund for Rural Development (EAFRD) includes Article (36) Cooperation that is potentially instrumental for realising the objectives of FOOD HARVEST 20204. The purpose of this report is to assess the scope and potential of Article 36 in the context of Irish agriculture and its findings have four key aspects. First, the main areas of confluence between Article 36 and primary policy objectives as set out in Food Harvest 2020 are identified. Second, a range of cooperation categories and types relevant to Article 36, many of which are operational in Ireland, are profiled. Third, drawing from case-studies of these co-operation types5, the operational characteristics of each type are presented, focusing on compatibility with Article 36. Possible supports that would encourage and assist the formation and operation of the cooperation types on a broad scale into the future, and also any possible constraints that would prevent success, are indicated. Fourth, a brief discussion of some key implementation considerations arising from the analysis overall is presented.Department of Agriculture, Food and the Marin

Privacy-Preserving Collaborative Learning through Feature Extraction

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations

Automata-based adaptive behavior for economic modeling using game theory

In this paper, we deal with some specific domains of applications to game theory. This is one of the major class of models in the new approaches of modelling in the economic domain. For that, we use genetic automata which allow to buid adaptive strategies for the players. We explain how the automata-based formalism proposed - matrix representation of automata with multiplicities - allows to define a semi-distance between the strategy behaviors. With that tools, we are able to generate an automatic processus to compute emergent systems of entities whose behaviors are represented by these genetic automata

Automata-based Adaptive Behavior for Economical Modelling Using Game Theory

In this chapter, we deal with some specific domains of applications to game theory. This is one of the major class of models in the new approaches of modelling in the economic domain. For that, we use genetic automata which allow to build adaptive strategies for the players. We explain how the automata-based formalism proposed - matrix representation of automata with multiplicities - allows to define semi-distance between the strategy behaviors. With that tools, we are able to generate an automatic processus to compute emergent systems of entities whose behaviors are represented by these genetic automata

Multi Site Coordination using a Multi-Agent System

A new approach of coordination of decisions in a multi site system is proposed. It is based this approach on a multi-agent concept and on the principle of distributed network of enterprises. For this purpose, each enterprise is defined as autonomous and performs simultaneously at the local and global levels. The basic component of our approach is a so-called Virtual Enterprise Node (VEN), where the enterprise network is represented as a set of tiers (like in a product breakdown structure). Within the network, each partner constitutes a VEN, which is in contact with several customers and suppliers. Exchanges between the VENs ensure the autonomy of decision, and guarantiee the consistency of information and material flows. Only two complementary VEN agents are necessary: one for external interactions, the Negotiator Agent (NA) and one for the planning of internal decisions, the Planner Agent (PA). If supply problems occur in the network, two other agents are defined: the Tier Negotiator Agent (TNA) working at the tier level only and the Supply Chain Mediator Agent (SCMA) working at the level of the enterprise network. These two agents are only active when the perturbation occurs. Otherwise, the VENs process the flow of information alone. With this new approach, managing enterprise network becomes much more transparent and looks like managing a simple enterprise in the network. The use of a Multi-Agent System (MAS) allows physical distribution of the decisional system, and procures a heterarchical organization structure with a decentralized control that guaranties the autonomy of each entity and the flexibility of the network

Organization of Multi-Agent Systems: An Overview

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.Comment: 12 page

The introduction of mandatory inter-municipal cooperation in small municipalities: preliminary lessons from Italy

PurposeThis article studies effects of mandatory inter-municipal cooperation (IMC) in small Italian municipalities. Data from 280 small Italian municipalities on effects of IMC in terms of higher efficiency, better effectiveness of local public services, and greater institutional legitimacy of the small municipalities participating in IMC have been investigated against four variables: size; geographical area; type of inter-municipal integration and IMC membership (the presence in the IMC of a bigger municipality, the so-called big brother).Design/methodology/approachData were gathered from a mail survey that was sent to a random sample of 1,360 chief financial officers acting in municipalities of under 5,000 inhabitants, stratified by size (0–1,000 and 1,001–5,000) and geographic area (North, Center, and South) criteria. To analyze dependency relationships between the three potential effects of participating in IMC and possible explanatory variables, we used a logistic regression model as the benefits were binarily categorized (presence or absence of benefits).FindingsFindings show that in more than two-thirds of the municipalities participating in IMC there were benefits in terms of costs reduction and better public services, whereas greater institutional legitimacy was detected in about half of the cases. Our statistical analysis with logistic regression highlighted that IMC type is particularly critical for explaining successful IMC. In particular, positive effects of IMC were mainly detected in those small municipalities that promoted a service delivery organization rather than participating in service delivery agreements or opting for mixed arrangements of joint public services delivery.Originality/valueThe paper focuses on small municipalities where studies are usually scant. Our analysis highlighted that the organizational setting is particularly critical for explaining successful IMC