Virgilio López-Morales

JAPIEST: An integral intelligent system for the diagnosis and control of tomatoes diseases and pests in hydroponic greenhouses

Automated Hydroponic Greenhouses represent novel food production systems which include modules for supervising the cultivated soil, packaging plans, as well as prevention, diagnosis and control of pests and diseases. In this setting, we propose the design and implementation of an Integral Intelligent System called JAPIEST, which is focused on the prevention, diagnosis and control of diseases that affect tomatoes (Licopersicum exculentum). Plants are farmed inside hydroponic greenhouses, whose particular conditions of temperature, humidity and nutrient consumption rates can influence directly the surge of plagues or diseases. It is relevant to detect and control the occurrence of any given pest or disease because plants are utterly sensitive to variations of environmental conditions and they have a short induced lifecycle. JAPIEST is a novel and valuable tool for farmers to make an early decision of the candidate disease, and then apply a suitable control treatment, based on Integrated Pest Management.

A distributed semantic network model for a collaborative intelligent system

A methodology based on topology theory to model a semantic network for a collaborative system is given. This framework is used to support the creation of a semantic network and to define the associated intelligent cooperative system. Our methodology is illustrated via a set of agents whose knowledge-base is a semantic network. By a series of functions applied on a base of entities, issued from the application domain, a family of sets are synthesized with their subspaces correlated. The resultant subspaces and their relations form a network of elementary and complex concepts that can be naturally represented with the IDEF1x language. A prototype Multi-Agent System (MAS), set up with the Zeus platform,1 was developed for the Process Plan domain, which was used as a case study. Full correspondence among the subspaces, the semantic network IDEF1x information model and the MAS implementation is obtained by employing this framework.

Intelligent and collaborative Multi-Agent System to generate and schedule production orders

The authors present a Multi-Agent System for constructing and releasing production orders. In a manufacturing enterprise, the generation of production orders consists in a set of coordinated tasks among departments. This has been achieved traditionally as a module of the Production Activity Control (PAC) system. However, classic PAC modules lack collaborative techniques and intelligent behaviour. Moreover, in real-life situations experienced planners take over traditional PAC systems, since the range of possibilities to actually build production orders increases exponentially. To contribute to production planning, we present an intelligent and collaborative Multi-Agent System (MAS), having coordinated two forms to emulate intelligence. The learning capability is achieved by means of a Feed-forward Artificial Neural Network (FANN) with the back-propagation algorithm. The FANN is embedded within a machine agent whose objective is to obtain the appropriate machine in order to comply with requirements coming from the sales department. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The MAS also consists of a coordinator and a spy. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. Finally, a scheduler agent schedules the production orders. The resultant MAS improves the current form to plan production in a factory dedicated to produce labels.

Cognitive communication in a multi-agent system for distributed process planning

This paper presents a framework for modelling and realisation of a Multi-Agent System (MAS) aimed at process planning. Firstly, the concepts of an ontology are constructed and partial subtasks are defined. Secondly, a knowledge base is implemented, regarding the relations stated in the ontology. Finally, a cooperative system is set that will establish speech acts to achieve the system goal by completing subtasks. The communication of knowledge and the achievement of subtasks are illustrated by two intelligent agents. They maintain a communication process that is inititated by a Part Agent. Once the message is formed, a response is obtained from the Operation Agent. The strength of this proposal is shown with the implementation of the MAS, in connection with a rule-based system.

Reliable Intervals Method in Decision-Based Support Models for Group Decision-Making

In this paper, a methodology to derive reliable intervals for multiplicative preference relations (or pairwise comparison matrices) satisfying consistency and consensus indexes is introduced. Our approach is proposed via a combination of numerical algorithms and a nonlinear optimization algorithm. A synthesis of reliable intervals is achieved, where group decision makers show evidence of these intervals to express flexibility in the manner of their preferences, while accomplishing some a priori decision targets, rules and advice given by their current framework. The algorithms are applied to some examples in order to illustrate our results and compare them with other methodologies.

MKMSIS: A Multi-agent Knowledge Management System for Industrial Sustainability

Industrial Sustainability in most companies face several problems when a firm tries to promote a greener image while focusing on maintaining profitability in a long-term approach. This is a necessary strategic approach, as customers are becoming more committed to buying products that consider environmental concerns and additional regulations need to be followed. However, traditional marketing and distribution methods, management tools and the homogenization of requirements fail to fully integrate the implication of environmental regulations into their processes. In this scenario, a system for supporting the integration of environmental concerns, endogenous and exogenous regulations, and market trends would be very well received. This is a complex task to be realized by just one system, even when a firm’s various departments have an efficient networked communication system. A Distributed Decision Making System (DDMS) could be a useful approach. This paper introduces a multi agent network for collaborative knowledge management. Since regulations and environmental issues are at the core of those processes and influence the final products, several sustainability phases can be addressed within this network, from the design to the marketing and distribution stages. The aim of our system is to deal with the management of these different phases in a collaborative platform by considering the sustainability knowledge related to several regulations.

Implementation of a Multi-agent Supervisory System for an Agricultural Products Sourcing Network

A Distributed Knowledge Management System (DKMS) for health control in hydroponic agricultural framework could improve production and subsequently be integrated into a sourcing network. The forecasted production could also be integrated into an electronic auction and thus allow for faster negotiations, with transparency in a previously agreed trading structure. Such a DKMS can address a variety of regulations (e.g., production control, package, labeling) and environmental concerns (Integrated Pest Management). In this supervisory intelligent network scenario, a high degree of collaboration and transparency promote the solution to such technical critical aspects. In this chapter, a multi-agent architecture for collaborative knowledge management in agricultural production in Mexico is introduced. Since diagnosis and control of diseases and pests, regulations, and environmental issues can be involved in the whole process, from the production control up to the marketing and distribution stages, agricultural sustainability can be later analyzed.