Learning to teach database design by trial and error

Proceedings of: 4th International Conference on Enterprise Information Systems (ICEIS 2002), Ciudad Real, Spain, April 3-6, 2002The definition of effective pedagogical strategies for coaching and tutoring students according to their needs in each moment is a high handicap in ITS design. In this paper we propose the use of a Reinforcement Learning (RL) model, that allows the system to learn how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. This technique avoids to define the teaching strategies by learning action policies that define what, when and how to teach. The model is applied to a database design ITS system, used as an example to illustrate all the concepts managed in the model

Agent-based distributed manufacturing scheduling: an ontological approach

The purpose of this paper is the need for self-sequencing operation plans in autonomous agents. These allow resolution of combinatorial optimisation of a global schedule, which consists of the fixed process plan jobs and which requires operations offered by manufacturers. The proposed agent-based approach was adapted from the bio-inspired metaheuristic- particle swarm optimisation (PSO), where agents move towards the schedule with the best global makespan. The research has achieved a novel ontology-based optimisation algorithm to allow agents to schedule operations whilst cutting down on the duration of the computational analysis, as well as improving the performance extensibility amongst others. The novelty of the research is evidenced in the development of a synchronised data sharing system allowing better decision-making resources with intrinsic manufacturing intelligence. The multi-agent platform is built upon the Java Agent Development Environment (JADE) framework. The operation research case studies were used as benchmarks for the evaluation of the proposed model. The presented approach not only showed a practical use case of a decentralised manufacturing system, but also demonstrated near optimal makespans compared to the operational research benchmarks

Resolució d’un problema de planificació dinàmica de treballs mitjançant aprenentatge per reforç

El problema Job Shop Scheduling, en el que s’ha de determinar l’ordre o seqüència òptima per a processar una sèrie de treballs en una sèrie de màquines, ha rebut una gran atenció en el món de l’organització industrial. Multitud d’heurístiques i models s’han anat proposant per tal de resoldre’l. Aquests algorismes però, acostumen a proposar solucions al problema simplificat, resolent-lo amb una sèrie de restriccions i limitacions que fan que la seva implementació sigui limitada. A més a més, la seva utilitat es veu reduïda quan es considera el problema dinàmic i no estàtic, considerant esdeveniments estocàstics com la fallada d’una màquina o l’arribada d’un nou treball. L’ús de models matemàtics exactes deixa de ser una proposta factible quan les dimensions del problema (número de màquines i treballs) augmenten. En aquest document es proposa una solució al problema d’assignació de treballs a màquines en un entorn dinàmic, on són presents esdeveniments estocàstics com la fallada o aturada d’una màquina i el temps d’arribada dels treballs a la zona de producció. Aquest problema s’aborda mitjançant l’Aprenentatge per Reforç (Reinforcement Learning), una branca de la intel·ligència artificial basada en la prova i error i l’experiènci

Using Multi-agent System for Dynamic Job Shop Scheduling.

Today's industries need more flexible scheduling systems able to produce new valid schedule in response to the modifications concerning orders, production processes and deliveries of materials. This paper introduces a multi-agent system applied to a job shop dynamic scheduling problem in which new production orders or deliveries arrive continuously and affect the already scheduled plan. We have solved the problem by: i) coupling reactive and pro-active agent behavior; and ii) implementing a stochastic method - simulated annealing - into agent's behavior. The job shop scheduling system is implemented using various types of agents whose interactions make the global state of the system move from a solution to another by continuously adapting to the changes from the environment. In this perspective, the interactions between the agents representing the client job orders, the production centers and the material stocks result in the assignment of operations and the plan for stock movements. Our experimental results show that, by modifying the classical agent-based message scheme, the integration of stochastic approach and multi-agent based technology could improve dynamic scheduling problems for a small to medium size problem space. (Résumé d'auteur

USING MULTI-AGENT SYSTEM FOR DYNAMIC JOB SHOP SCHEDULING 1

Abstract: Today’s industries need more flexible scheduling systems able to produce new valid schedule in response to the modifications concerning orders, production processes and deliveries of materials. This paper introduces a multi-agent system applied to a job shop dynamic scheduling problem in which new production orders or deliveries arrive continuously and affect the already scheduled plan. We have solved the problem by: i) coupling reactive and pro-active agent behavior; and ii) implementing a stochastic method- simulated annealing- into agent’s behavior. The job shop scheduling system is implemented using various types of agents whose interactions make the global state of the system move from a solution to another by continuously adapting to the changes from the environment. In this perspective, the interactions between the agents representing the client job orders, the production centers and the material stocks result in the assignment of operations and the plan for stock movements. Our experimental results show that, by modifying the classical agent-based message scheme, the integration of stochastic approach and multi-agent based technology could improve dynamic scheduling problems for a small to medium size problem space.