CSM-385 Cooperation in Competitions - Constraint Propagation Strategies in Chain-bargaining

Electronic business motivates automatic bargaining: computers may not be as good bargainers as expert human bargainers, but by talking to a large number of traders on the Internet, they stand a better chance of getting good deals. While an end-seller may be interested in getting the highest profit from each negotiation, traders have to balance between making large profits in few deals (thus missing opportunities in the failed negotiations) and making smaller profits in larger number of deals. When a deal is to be constructed through a chain of middlemen, these middlemen have to cooperate (in order to construct the deal) while trying to maximize their own profits. The middlemen can be seen as propagating constraints along the chain, with the aim to maximize its profit while satisfying all the bargainers' constraints (failing that, the chain will break down). In this paper, a simple chainbargaining problem is defined. It is used to study constraint propagation strategies, which are essential components of automatic bargaining

Quality Diversity: Harnessing Evolution to Generate a Diversity of High-Performing Solutions

Evolution in nature has designed countless solutions to innumerable interconnected problems, giving birth to the impressive array of complex modern life observed today. Inspired by this success, the practice of evolutionary computation (EC) abstracts evolution artificially as a search operator to find solutions to problems of interest primarily through the adaptive mechanism of survival of the fittest, where stronger candidates are pursued at the expense of weaker ones until a solution of satisfying quality emerges. At the same time, research in open-ended evolution (OEE) draws different lessons from nature, seeking to identify and recreate processes that lead to the type of perpetual innovation and indefinitely increasing complexity observed in natural evolution. New algorithms in EC such as MAP-Elites and Novelty Search with Local Competition harness the toolkit of evolution for a related purpose: finding as many types of good solutions as possible (rather than merely the single best solution). With the field in its infancy, no empirical studies previously existed comparing these so-called quality diversity (QD) algorithms. This dissertation (1) contains the first extensive and methodical effort to compare different approaches to QD (including both existing published approaches as well as some new methods presented for the first time here) and to understand how they operate to help inform better approaches in the future. It also (2) introduces a new technique for encoding neural networks for evolution with indirect encoding that contain multiple sensory or output modalities. Further, it (3) explores the idea that QD can act as an engine of open-ended discovery by introducing an expressive platform called Voxelbuild where QD algorithms continually evolve robots that stack blocks in new ways. A culminating experiment (4) is presented that investigates evolution in Voxelbuild over a very long timescale. This research thus stands to advance the OEE community\u27s desire to create and understand open-ended systems while also laying the groundwork for QD to realize its potential within EC as a means to automatically generate an endless progression of new content in real-world applications

DNAgents: Genetically Engineered Intelligent Mobile Agents

Mobile agents are a useful paradigm for network coding providing many advantages and disadvantages. Unfortunately, widespread adoption of mobile agents has been hampered by the disadvantages, which could be said to outweigh the advantages. There is a variety of ongoing work to address these issues, and this is discussed. Ultimately, genetic algorithms are selected as the most interesting potential avenue. Genetic algorithms have many potential benefits for mobile agents. The primary benefit is the potential for agents to become even more adaptive to situational changes in the environment and/or emergent security risks. There are secondary benefits such as the natural obfuscation of functions inherent to genetic algorithms. Pitfalls also exist, namely the difficulty of defining a satisfactory fitness function and the variable execution time of mobile agents arising from the fact that it exists on a network. DNAgents 1.0, an original application of genetic algorithms to mobile agents is implemented and discussed, and serves to highlight these difficulties. Modifications of traditional genetic algorithms are also discussed. Ultimately, a combination of genetic algorithms and artificial life is considered to be the most appropriate approach to mobile agents. This allows the consideration of agents to be organisms, and the network to be their environment. Towards this end, a novel framework called DNAgents 2.0 is designed and implemented. This framework allows the continual evolution of agents in a network without having a seperate training and deployment phase. Parameters for this new framework were defined and explored. Lastly, an experiment similar to DNAgents 1.0 is performed for comparative purposes against DNAgents 1.0 and to prove the viability of this new framework

A distributed approach for AGV scheduling

Se adjuntan 6 archivos de Simio como soporte que contienen 6 modelos desarrollados durante el trabajo de grado. Además, se anexa un link que redirecciona a un sitio web seguro (Microsoft Stream) dónde se encuentra un video explicativo del modelo final de Simio desarrollado para el trabajo. Adicionalmente se adjuntan 2 archivos Excel, uno que contiene los modelos estáticos desarrollados (heurística y metaheurística) para validación del modelo final y otro que contiene análisis estadísticos realizados Por último, se anexan todos los documentos solicitados por la dirección de Trabajo de Grado en formato PDF junto con 2 adicionales que corresponden a memoria de cálculos para validaciones estadísticas y resultados de modelos estáticos.The implementation of Industry 4.0, where robotics mix with information and communication technologies to increase efficiency in Flexible Manufacturing Systems (FMS), is at its peak. Automated Guided Vehicles (AGVs) have become increasingly popular because they increase transportation flexibility, reducing transportation costs and overall process times. The AGV scheduling problem has been mostly pointed towards time optimization only using centralized approaches where the scheduling of production does not change and it is considered static. FMS in real life are dynamic environments that demand flexibility, as well as reactivity, to deal with changes in production conditions, such as machine breakdowns, rush orders, layout changes, lack of raw materials, among others. Therefore, there is a need for a dynamic approach to the AGV scheduling problem that addresses real life unexpected situations more efficiently, aiming for time saving at the same time. The purpose of this project is to design and implement, in a simulation environment, a distributed approach to the AGV scheduling problem that deals better with real-life FMS changing conditions. Results show that although our approach is based on the MSM heuristic, good performance measures in real time were obtained comparing with other optimization algorithms.Ingeniero (a) IndustrialPregrad

Framework para escalonamento distribuído de processos utilizando sistema multiagentes em sistemas de produção

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2009O estudo de técnicas de escalonamento de processos remete à criação dos primeiros sistemas operacionais (SO), com os algoritmos escalonadores de processos com e sem preempção. Porém a utilização de escalonadores de processo atinge outras áreas além dos SO, afeta todos os problemas onde há um conjunto de tarefas a serem executadas e um conjunto de unidades executantes. O tempo de execução final das tarefas é diretamente afetado pela seqüência de execução adotada, como é o caso dos sistemas de produção, que necessitam de informação em tempo real, para a execução de tarefas ou para o diagnóstico de problemas, com o objetivo da rápida tomada de decisão. Desse modo é necessário precisão e agilidade no processamento, nas mudanças de prioridades, e principalmente, eficiência no gerenciamento da informação. Este trabalho propõe um framework para escalonamento distribuído de processos, utilizando a teoria de agentes e a técnica heurística de busca, Algoritmos Genéticos (AG). Na modelagem do framework e aplicação foi utilizado a metodologia MaSE (Multi-agent System Engineering), que especifica etapas para análise e projeto de sistemas multiagentes. O desenvolvimento do framework e aplicação foi integrado à plataforma JADE (Java Agent Development Framework), utilizando as ontologias desenvolvidas no editor Protégé. A fim de validar o framework, desenvolveu-se um estudo de caso utilizando o framework HIPS (Hybrid Intelligent Process Scheduler) e os resultados e limitações obtidos com esse estudo de caso, comparados a outro escalonamento de processos.The study of techniques for processes schedulers is linked to the creation of the first operational systems (OS), with the process schedulers algorithms with and without preemption. However, the use of processes schedulers reaches other areas besides OS. It affects all the problems where there is a set of tasks to be executed and a set of executing units. The tasks final execution time is directly affected by the execution sequence that is adopted, as in the case of production systems, which need information in real time, for the execution of tasks or for the diagnosis of problems aiming fast decision making. Thus, precision and agility in processing, in changing priorities and mainly, efficiency in managing the information are needed. This work proposes a framework for processes distributed scheduler, using the agents theory and the Genetic Algorithms (GA) heuristic search technique. In the framework modeling and application the MaSE (Multi-agent System Engineering) was used, which specifies stages for analyses and project of multi agents systems. The framework development and application were integrated to a JADE (Java Agent Development Framework), using the ontology developed in the Protégé editor. For the framework validation, the study of a case was developed using the HIPS (Hybrid Intelligent Process Scheduler) framework and the results and limitations obtained in this study were compared to another process scheduler

Evolution of microgrids with converter-interfaced generations: Challenges and opportunities

© 2019 Elsevier Ltd Although microgrids facilitate the increased penetration of distributed generations (DGs) and improve the security of power supplies, they have some issues that need to be better understood and addressed before realising the full potential of microgrids. This paper presents a comprehensive list of challenges and opportunities supported by a literature review on the evolution of converter-based microgrids. The discussion in this paper presented with a view to establishing microgrids as distinct from the existing distribution systems. This is accomplished by, firstly, describing the challenges and benefits of using DG units in a distribution network and then those of microgrid ones. Also, the definitions, classifications and characteristics of microgrids are summarised to provide a sound basis for novice researchers to undertake ongoing research on microgrids

An Ex-Ante Rational Distributed Resource Allocation System using Transfer of Control Strategies for Preemption with Applications to Emergency Medicine

Within the artificial intelligence subfield of multiagent systems, one challenge that arises is determining how to efficiently allocate resources to all agents in a way that maximizes the overall expected utility. In this thesis, we explore a distributed solution to this problem, one in which the agents work together to coordinate their requests for resources and which is considered to be ex-ante rational: in other words, requiring agents to be willing to give up their current resources to those with greater need by reasoning about what is for the common good. Central to our solution is allowing for preemption of tasks that are currently occupying resources; this is achieved by introducing a concept from adjustable autonomy multiagent systems known as a transfer of control (TOC) strategy. In essence a TOC strategy is a plan of an agent to acquire resources at future times, and can be used as a contingency plan that an agent will execute if it loses its current resource. The inclusion of TOC strategies ultimately provides for a greater optimism among agents about their future resource acquisitions, allowing for more generous behaviours, and for agents to more frequently agree to relinquish current resources, resulting in more effective preemption policies. Three central contributions arise. The first is an improved methodology for generating transfer of control strategies efficiently, using a dynamic programming approach, which enables a more effective employment of TOCs in our resource allocation solution. The second is an important clarification of the value of integrating learning techniques in order for agents to acquire improved estimates of the costs of preemption. The last is a validation of the overall multiagent resource allocation (MARA) solution, using simulations which show quantifiable benefits of our novel approach. In particular, we consider in detail the emergency medical application of mass casualty incidents and are able to demonstrate that our approach of integrating transfer of control strategies results in effective allocation of patients to doctors: ones which in simulations re- sult in dramatically fewer patients in a critical healthstate than are produced by competing MARA algorithms. In short, we offer a principled solution to the problem of preemption, allowing the elimination of a source of inefficiencies in fully distributed multiagent resource allocation systems; a faster method for generation of transfer of control strategies; and a convincing application of the system to a real world problem where human lives are at stake

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p