Applying biological paradigms to emerge behaviour in RoboCup Rescue team

This paper presents a hybrid behaviour process for performing collaborative tasks and coordination capabilities in a rescue team. RoboCup Rescue simulator and its associated international competition are used as the testbed for our proposal. Unlike other published work in this field one of our main concerns is having good results on RoboCup Rescue championships by emerging behaviour in agents using a biological paradigm. The benefit comes from the hierarchic and parallel organisation of the mammalian brain. In our behaviour process, Artificial Neural Networks are used in order to make agents capable of learning information from the environment. This allows agents to improve several algorithms like their Path Finding Algorithm to find the shortest path between two points. Also, we aim to filter the most important messages that arise from the environment, to make the right choice on the best path planning among many alternatives, in a short time. A policy action was implemented using Kohonen's network, Dijkstra's and D* algorithm. This policy has achieved good results in our tests, getting our team classified for RoboCup Rescue Simulation League 2005

Modelo de estratégia e coordenação genérico para sistemas multi-agente

Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Jose Nuno Panelas Nunes LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

A Framework for Learning by Demonstration in Multi-teacher Multi-robot Scenarios

As robots become more accessible to humans, more intuitive and human-friendly ways of programming them with interactive and group-aware behaviours are needed. This thesis addresses the gap between Learning by Demonstration and Multi-robot systems. In particular, this thesis tackles the fundamental problem of learning multi-robot cooperative behaviour from concurrent multi-teacher demonstrations, problem which had not been addressed prior to this work. The core contribution of this thesis is the design and implementation of a novel, multi- layered framework for multi-robot learning from simultaneous demonstrations, capable of deriving control policies at two different levels of abstraction. The lower level learns models of joint-actions at trajectory level, adapting such models to new scenarios via feature mapping. The higher level extracts the structure of cooperative tasks at symbolic level, generating a sequence of robot actions composing multi-robot plans. To the best of the author's knowledge, the proposed framework is the first Learning by Demonstration system to enable multiple human demonstrators to simultaneously teach group behaviour to multiple robots learners. A series of experimental tests were conducted using real robots in a real human workspace environment. The results obtained from a comprehensive comparison confirm the appli- cability of the joint-action model adaptation method utilised. What is more, the results of several trials provide evidence that the proposed framework effectively extracts rea- sonable multi-robot plans from demonstrations. In addition, a case study of the impact of human communication when using the proposed framework was conducted, suggesting no evidence that communication affects the time to completion of a task, but may have a positive effect on the extraction multi-robot plans. Furthermore, a multifaceted user study was conducted to analyse the aspects of user workload and focus of attention, as well as to evaluate the usability of the teleoperation system, highlighting which parts were necessary to be improved

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