A Simulation-Based Layered Framework Framework for the Development of Collaborative Autonomous Systems

The purpose of this thesis is to introduce a simulation-based software framework that facilitates the development of collaborative autonomous systems. Significant commonalities exist in the design approaches of both collaborative and autonomous systems, mirroring the sense, plan, act paradigm, and mostly adopting layered architectures. Unfortunately, the development of such systems is intricate and requires low-level interfacing which significantly detracts from development time. Frameworks for the development of collaborative and autonomous systems have been developed but are not flexible and center on narrow ranges of applications and platforms. The proposed framework utilizes an expandable layered structure that allows developers to define a layered structure and perform isolated development on different layers. The framework provides communication capabilities and allows message definition in order to define collaborative behavior across various applications. The framework is designed to be compatible with many robotic platforms and utilizes the concept of robotic middleware in order to interface with robots; attaching the framework on different platforms only requires changing the middleware. An example Fire Brigade application that was developed in the framework is presented; highlighting the design process and utilization of framework related features. The application is simulation-based, relying on kinematic models to simulate physical actions and a virtual environment to provide access to sensor data. While the results demonstrated interesting collaborative behavior, the ease of implementation and capacity to experiment by swapping layers is particularly noteworthy. The framework retains the advantages of layered architectures and provides greater flexibility, shielding developers from intricacies and providing enough tools to make collaboration easy to perform

Behavior of bucket brigade in an order-picking system under the effect of fatigue

Il lavoro considera l’effetto della fatica sugli operatori in un bucket-brigade in un sistema di order-picking. Lo scopo dell’ elaborato è quello di studiare il comportamento di tutti i possibili tipi di bucket brigade e decidere quali sono i più performanti. I risultati sono stati ottenuti sia numericamente con MATLAB che analiticamente. Oltre a presentare i risultati ottenuti, sono fornite anche delle istruzioni che il manager deve seguire per massimizzare la performance del sistema

‘We Are the Monitors Now’: Experiential Knowledge, Transcorporeality and Environmental Justice

Residents of pollution hotspots often take on projects in ‘citizen science’, or popularepidemiology, in an effort to marshal the data that can prove their experience of the pollution to the relevant authorities. Sometimes these tactics, such as pollution logs or bucket brigades, take advantage of residents’ spatially ordered and finely honed experiential and sensory knowledge of the places they inhabit. But putting that knowledge into conversation with law requires them to mobilize a new, ‘foreign’ set of tools, primarily oriented to the observation, measurement and sampling of pollution according to conventional scientific standards. Here, I employ qualitative empirical methods in two case studies of communities ‘downwind’ of Canada’s contested tar sands region to demonstrate that the knowledge that is crucial to these citizen science strategies is not only local, situated and experiential in origin but also collectively generated and held. I draw on the notion of transcorporeality, emanating from feminist theory of the body, to demonstrate that the knowledge offered to law through these efforts often represents a fluid merger of experiential and conventional ways of knowing, posing a productive challenge to the strictly positive notions of science and evidence dominant in legal proceedings

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems. What makes it challenging to use a decentralized approach to collectively optimize a central objective is the difficulty in characterizing the equilibrium strategy profile of non-cooperative games. To overcome this challenge, we design a mechanism for defining the learning environment of each agent for which we know that the optimal solution for the global objective coincides with a Nash equilibrium strategy profile of the agents optimizing their own local objectives. The society functions as an economy of agents that learn the credit assignment process itself by buying and selling to each other the right to operate on the environment state. We derive a class of decentralized reinforcement learning algorithms that are broadly applicable not only to standard reinforcement learning but also for selecting options in semi-MDPs and dynamically composing computation graphs. Lastly, we demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.Comment: 18 pages, 13 figures, accepted to the International Conference on Machine Learning (ICML) 202

Constructivism, epistemology and information processing

The author analyzes the main models of artificial intelligence which deal with the transition from one stage to another, a central problem in development. He describes the contributions of rule-based systems and connectionist systems to an explanation of this transition. He considers that Artificial Intelligence models, in spite of their limitations, establish fruitful points of contact with the constructivist position.El autor analiza los principales modelos de inteligencia artificial que dan cuenta del paso de la transición de un estudio a otro, problema central del desarrollo. Describe y señala las aportaciones de los sistemas basados en reglas así como de los sistemas conexionistas para explicar dicha transición. Considera que los modelos de inteligencia artificial, a pesar de sus limitaciones, permiten establecer puntos de contacto muy fructiferos con la posición constructivista