Reasoning about abductive inferences in BDI agents

The capability of a computational system to deal with unexpected, changing situations and limited perception of the environment is becoming more a more relevant, in oder to make systems flexible and more reliable. Multi-agent Systems offer a computing paradigm where properties such as autonomy, adaptability or flexibility are basic in the construction of agent-based solutions. However most of current implementations are not flexible enough to cope with important changes in the environment or information loss. In this paper we propose to introduce abductive reasoning mechanisms in BDI agents and show how such agents are able to operate with partial models of the environment.Preprin

Exploring how digital media technologies can foster Saudi EFL students' English language learning

Digital Media Technologies (DMTs) has been inspiring people, especially younger generations, for decades. In education, DMTs usage has been investigated as a learning tool. In recent years, studies have been conducted to examine the affordances of DMTs in the context of learning English as a foreign language (EFL). Research has shown that there is a relationship between DMTs usage and intentional learning, as the latter has been argued to be an important aspect of learning. This study aims to understand high-school students’ use of DMTs for fostering EFL intentional learning, especially outside the classroom in the Saudi context. To achieve this goal, a mixed-method research approach was applied. The quantitative data was collected through an online survey that was distributed to Year 12 Saudi male students (n= 350). The qualitative data was collected with students through two phases: the first phase consisted of semi-structured focus group interviews (n= 24) while the second was an online journal (n= 6). The results have shown that Saudi high-school students were highly engaged with DMTs and intentionally use several types of DMTs for learning purposes

Plan Acquisition Through Intentional Learning in BDI Multi-Agent Systems

Multi-Agent Systems (MAS), a technique emanating from Distributed Artificial Intelligence, is a suitable technique to study complex systems. They make it possible to represent and simulate both elements and interrelations of systems in a variety of domains. The most commonly used approach to develop the individual components (agents) within MAS is reactive agency. However, other architectures, like cognitive agents, enable richer behaviours and interactions to be captured and modelled. The well-known Belief-Desire-Intentions architecture (BDI) is a robust approach to develop cognitive agents and it can emulate aspects of autonomous behaviour and is thus a promising tool to simulate social systems. Machine Learning has been applied to improve the behaviour of agents both individually or collectively. However, the original BDI model of agency, is lacking learning as part of its core functionalities. To cope with learning, the BDI agency has been extended by Intentional Learning (IL) operating at three levels: belief adjustment, plan selection, and plan acquisition. The latter makes it possible to increase the agent’s catalogue of skills by generating new procedural knowledge to be used onwards. The main contributions of this thesis are: a) the development of IL in a fully-fledged BDI framework at the plan acquisition level, b) extending IL from the single-agent case to the collective perspective; and c) a novel framework that melts reactive and BDI agents through integrating both MAS and Agent-Based Modelling approaches, it allows the configuration of diverse domains and environments. Learning is demonstrated in a test-bed environment to acquire a set of plans that drive the agent to exhibit behaviours such as target-searching and left-handed wall-following. Learning in both decision strata, single and collective, is tested in a more challenging and socially relevant environment: the Disaster-Rescue problem

Self-adaptive multi-agent systems for aided decision-making : an application to maritime surveillance

L'activité maritime s'est fortement développée ces dernières années et sert de support à de nombreuses activités illicites. Il est devenu nécessaire que les organismes impliqués dans la surveillance maritime disposent de systèmes efficaces pour les aider à identifier ces activités illicites. Les Systèmes de Surveillance Maritime doivent observer de manière efficace un espace maritime large, à identifier des anomalies de comportement des navires évoluant dans l'espace en question, et à déclencher des alertes documentées si ces anomalies amènent à penser que les navires ont un comportement suspect. Nous proposons un modèle générique de système multi-agents, que nous appelons MAS4AT, capable de remplir deux des différents rôles d'un système de surveillance : la représentation numérique des comportements des entités surveillées et des mécanismes d'apprentissage pour une meilleure efficacité. MAS4AT est intégré au système I2C.The maritime activity has widely grow in the last few years and is the witness of several illegal activities. It has become necessary that the organizations involved in the maritime surveillance possess efficient systems to help them in their identification. The maritime surveillance systems must observe a wide maritime area, identify the anomalies in the behaviours of the monitored ships et trigger alerts when these anomalies leads to a suspicious behavior. We propose a generic agent model, called MAS4AT, able to fulfil two main roles of a surveillance system: the numerical representation of the behaviours of the monitored entities and learning mechanisms for a better efficiency. MAS4AT is integrated in the system I2C

Intentional learning agent architecture

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Intentional Learning Agent Architecture/

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