Melting Pot 2.0

Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsistic" approaches, which do not consider interactions between agents. Melting Pot is a research tool developed to facilitate work on multi-agent artificial intelligence, and provides an evaluation protocol that measures generalization to novel social partners in a set of canonical test scenarios. Each scenario pairs a physical environment (a "substrate") with a reference set of co-players (a "background population"), to create a social situation with substantial interdependence between the individuals involved. For instance, some scenarios were inspired by institutional-economics-based accounts of natural resource management and public-good-provision dilemmas. Others were inspired by considerations from evolutionary biology, game theory, and artificial life. Melting Pot aims to cover a maximally diverse set of interdependencies and incentives. It includes the commonly-studied extreme cases of perfectly-competitive (zero-sum) motivations and perfectly-cooperative (shared-reward) motivations, but does not stop with them. As in real-life, a clear majority of scenarios in Melting Pot have mixed incentives. They are neither purely competitive nor purely cooperative and thus demand successful agents be able to navigate the resulting ambiguity. Here we describe Melting Pot 2.0, which revises and expands on Melting Pot. We also introduce support for scenarios with asymmetric roles, and explain how to integrate them into the evaluation protocol. This report also contains: (1) details of all substrates and scenarios; (2) a complete description of all baseline algorithms and results. Our intention is for it to serve as a reference for researchers using Melting Pot 2.0.Comment: 59 pages, 54 figures. arXiv admin note: text overlap with arXiv:2107.0685

Aprendizagem de coordenação em sistemas multi-agente

The ability for an agent to coordinate with others within a system is a valuable property in multi-agent systems. Agents either cooperate as a team to accomplish a common goal, or adapt to opponents to complete different goals without being exploited. Research has shown that learning multi-agent coordination is significantly more complex than learning policies in singleagent environments, and requires a variety of techniques to deal with the properties of a system where agents learn concurrently. This thesis aims to determine how can machine learning be used to achieve coordination within a multi-agent system. It asks what techniques can be used to tackle the increased complexity of such systems and their credit assignment challenges, how to achieve coordination, and how to use communication to improve the behavior of a team. Many algorithms for competitive environments are tabular-based, preventing their use with high-dimension or continuous state-spaces, and may be biased against specific equilibrium strategies. This thesis proposes multiple deep learning extensions for competitive environments, allowing algorithms to reach equilibrium strategies in complex and partially-observable environments, relying only on local information. A tabular algorithm is also extended with a new update rule that eliminates its bias against deterministic strategies. Current state-of-the-art approaches for cooperative environments rely on deep learning to handle the environment’s complexity and benefit from a centralized learning phase. Solutions that incorporate communication between agents often prevent agents from being executed in a distributed manner. This thesis proposes a multi-agent algorithm where agents learn communication protocols to compensate for local partial-observability, and remain independently executed. A centralized learning phase can incorporate additional environment information to increase the robustness and speed with which a team converges to successful policies. The algorithm outperforms current state-of-the-art approaches in a wide variety of multi-agent environments. A permutation invariant network architecture is also proposed to increase the scalability of the algorithm to large team sizes. Further research is needed to identify how can the techniques proposed in this thesis, for cooperative and competitive environments, be used in unison for mixed environments, and whether they are adequate for general artificial intelligence.A capacidade de um agente se coordenar com outros num sistema é uma propriedade valiosa em sistemas multi-agente. Agentes cooperam como uma equipa para cumprir um objetivo comum, ou adaptam-se aos oponentes de forma a completar objetivos egoístas sem serem explorados. Investigação demonstra que aprender coordenação multi-agente é significativamente mais complexo que aprender estratégias em ambientes com um único agente, e requer uma variedade de técnicas para lidar com um ambiente onde agentes aprendem simultaneamente. Esta tese procura determinar como aprendizagem automática pode ser usada para encontrar coordenação em sistemas multi-agente. O documento questiona que técnicas podem ser usadas para enfrentar a superior complexidade destes sistemas e o seu desafio de atribuição de crédito, como aprender coordenação, e como usar comunicação para melhorar o comportamento duma equipa. Múltiplos algoritmos para ambientes competitivos são tabulares, o que impede o seu uso com espaços de estado de alta-dimensão ou contínuos, e podem ter tendências contra estratégias de equilíbrio específicas. Esta tese propõe múltiplas extensões de aprendizagem profunda para ambientes competitivos, permitindo a algoritmos atingir estratégias de equilíbrio em ambientes complexos e parcialmente-observáveis, com base em apenas informação local. Um algoritmo tabular é também extendido com um novo critério de atualização que elimina a sua tendência contra estratégias determinísticas. Atuais soluções de estado-da-arte para ambientes cooperativos têm base em aprendizagem profunda para lidar com a complexidade do ambiente, e beneficiam duma fase de aprendizagem centralizada. Soluções que incorporam comunicação entre agentes frequentemente impedem os próprios de ser executados de forma distribuída. Esta tese propõe um algoritmo multi-agente onde os agentes aprendem protocolos de comunicação para compensarem por observabilidade parcial local, e continuam a ser executados de forma distribuída. Uma fase de aprendizagem centralizada pode incorporar informação adicional sobre ambiente para aumentar a robustez e velocidade com que uma equipa converge para estratégias bem-sucedidas. O algoritmo ultrapassa abordagens estado-da-arte atuais numa grande variedade de ambientes multi-agente. Uma arquitetura de rede invariante a permutações é também proposta para aumentar a escalabilidade do algoritmo para grandes equipas. Mais pesquisa é necessária para identificar como as técnicas propostas nesta tese, para ambientes cooperativos e competitivos, podem ser usadas em conjunto para ambientes mistos, e averiguar se são adequadas a inteligência artificial geral.Apoio financeiro da FCT e do FSE no âmbito do III Quadro Comunitário de ApoioPrograma Doutoral em Informátic

Human Behavior Experimentation and Participation in Scientific Activities in the Wild

La cooperació és un dels trets del comportament que defineixen els éssers humans, però, encara estem tractant de comprendre per què els humans cooperem. Els experiments conductuals estan dissenyats per donar llum sobre els mecanismes de cooperació i altres trets del comportament. Aquesta dissertació es centra en avançar en el camp de l'experimentació conductual utilitzant les pràctiques de ciència ciutadana, i es divideix en dos blocs. En el primer, presentem dues plataformes, una que permet estudiar com promoure el pensament científic i la participació científica, i l'altra que permet estudiar els trets del comportament humà amb un conjunt de jocs de comportament. Les dues plataformes estàn dissenyades per ajudar a crear experiments en el camp i per fomentar la participació en el marc de la ciència ciutadana. En el segon bloc avaluem les plataformes a través d'un conjunt d'experiments, i analitzem l'existència de patrons de comportament. Primer, vam estudiar la solidesa de la plataforma observant si sorgeixen estratègies iguals en rèpliques del mateix experiment realitzat amb diferents mostres de població. En el segon experiment, analitzem els patrons de comportament que emergeixen quan els participants s'enfronten a un conjunt de dilemes socials. Els dos últims experiments són "collective-risk dilemmas" sobre el canvi climàtic. En un, estudiem com les desigualtats de recursos generen comportaments injustos. L'altre es porta a terme dins d'un ecosistema concret per estudiar les tensions que hi ha entre els diferents actors del col·lectiu. Tenint en compte els resultats dels experiments, podem entendre com ens comportem quan enfrontem dilemes socials i, en conseqüència, avaluar els trets de comportament i l'aparició de patrons de comportament. Els dissenys, els resultats i la metodologia d'anàlisi presentats en aquest treball ajudaran a establir les bases per a futurs experiments de comportament al camp.La cooperación es uno de los rasgos de comportamiento que definen a los seres humanos, sin embargo, todavía estamos tratando de comprender por qué los humanos cooperamos. Los experimentos conductuales están diseñados para arrojar luz sobre los mecanismos de cooperación y otros rasgos de comportamiento. Esta disertación se centra en avanzar en el campo de la experimentación conductual utilizando las prácticas de ciencia ciudadana, y se divide en dos bloques. En el primero, presentamos dos plataformas, una que permite estudiar cómo promover el pensamiento científico y la participación científica, y otra para estudiar los rasgos del comportamiento humano con un conjunto de juegos de comportamiento. Ambas plataformas están diseñadas para ayudar a crear experimentos en el campo y para fomentar la participación en el marco de la ciencia ciudadana. En el segundo bloque evaluamos las plataformas a través de un conjunto de experimentos, y analizamos la existencia de patrones de comportamiento. Primero, estudiamos la solidez de la plataforma al observar si surgen estrategias iguales en réplicas del mismo experimento realizado con diferentes muestras de población. En el segundo experimento, analizamos los patrones de comportamiento que emergen cuando los participantes enfrentan un conjunto de dilemas sociales. Los dos últimos experimentos son "collective-risk dilemmas" sobre el cambio climático. En uno, estudiamos cómo las desigualdades de recursos generan comportamientos injustos. El otro se lleva a cabo dentro de un ecosistema concreto para estudiar las tensiones que existen entre los diferentes actores del colectivo. Teniendo en cuenta los resultados de los experimentos, podemos entender cómo nos comportamos cuando enfrentamos dilemas sociales y, en consecuencia, evaluar los rasgos de comportamiento y la aparición de patrones de comportamiento. Los diseños, los resultados y la metodología de análisis presentados en este trabajo ayudarán a establecer las bases para futuros experimentos de comportamiento en el campo.Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments are designed to shed light into the mechanisms behind cooperation -- and other behavioral traits. This dissertation is focused on advancing the field of behavioral experimentation using experiments based on citizen science, and it is divided in two blocks. In the first, we present two platforms, one to understand how it can serve as a catalyst to promote of scientific thinking and engage in science, and another to study traits of human behavior with a suite of behavioral games. Both platforms are designed to help creating experiments in the wild and to encourage the participation within the framework of citizen science. In the second block we evaluate the platforms through a set of experiments, and we analyze the existence of behavioral patterns. First, we study the robustness of the platform by looking whether equal strategies emerge in replicas of the same experiment performed with different population samples. In the second experiment we analyze the behavioral patterns that emerge when participants face a set of social dilemmas. The last two experiments are collective-risk dilemmas framed in climate change. In one, we study how the resource inequalities generate unfair behaviors. The other is carried out within a given ecosystem to study the tensions that exist between actors of the collective. Considering the experiments' results, we can comprehend how we behave when we face social dilemmas, and consequently evaluate behavioral traits and the emergence of behavioral patterns. The designs, the results and the methodology of analysis presented in this work will help set the basis for future behavioral experiments in the field

Cooperation and Social Dilemmas with Reinforcement Learning

Cooperation between humans has been foundational for the development of civilisation and yet there are many questions about how it emerges from social interactions. As artificial agents begin to play a more significant role in our lives and are introduced into our societies, it is apparent that understanding the mechanisms of cooperation is important also for the design of next-generation multi-agent AI systems. Indeed, this is particularly important in the case of supporting cooperation between self-interested AI agents. In this thesis, we focus on the analysis of the application of mechanisms that are at the basis of human cooperation to the training of reinforcement learning agents. Human behaviour is a product of cultural norms, emotions and intuition amongst other things: we argue it is possible to use similar mechanisms to deal with the complexities of multi-agent cooperation. We outline the problem of cooperation in mixed-motive games, also known as social dilemmas, and we focus on the mechanisms of reputation dynamics and partner selection, two mechanisms that have been strongly linked to indirect reciprocity in Evolutionary Game Theory. A key point that we want to emphasise is the fact we assume no prior knowledge and explicit definition of strategies, which instead are fully learnt by the agents during the games. In our experimental evaluation, we demonstrate the benefits of applying these mechanisms to the training process of the agents, and we compare our findings with results presented in a variety of other disciplines, including Economics and Evolutionary Biology

Multidisciplinary perspectives on Artificial Intelligence and the law

This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

Kindernomics: The Developmental Origins of Other-Regarding Preferences in Children

PhDPeople systematically allow others’ outcomes to affect their decisions. These tendencies, known as other-regarding preferences, are irrational according to traditional models of economics, and yet their existence is increasingly well-documented. This picture, however, is unbalanced. More attention has been devoted to examining positive other-regarding preferences, behaviours which help others, than is the case with negative other-regarding preferences, behaviours which harm them. This thesis aimed to help rectify this imbalance by using economic experiments to study the emergence and development of negative other-regarding preferences, and the motivations which lay behind them, in childhood, in a sample aged from 4-13 years of age. Experiments 1 and 2 focused upon costly punishment in a variant of the ultimatum game. Only children aged 6-7 years and upwards were observed to consistently show negative other-regarding preferences, which generally increased with age in both experiments. Experiment 3 used the moonlighting game to compare children’s positive and negative other-regarding preferences, in the form of their willingness to make reciprocal responses to pro- and anti-social behaviours. Negative reciprocity exceeded positive reciprocity in children of all ages, and the two traits were not observed to be correlated within-subjects. Experiments 4 and 5 examined whether negative other-regarding preferences would undermine cooperation in two mutualistic contexts, the battle of the sex game and the stag hunt, and also in the chicken game. In all contexts, pairs of children failed to achieve cooperative outcomes. The implications of these findings are discussed. There was strong evidence of basic fairness concerns such as disadvantageous inequity aversion and relative comparisons affecting these results, but less evidence of higher fairness concerns or of internalised standards of normative behaviour. Negative other-regarding preferences were ubiquitous throughout pre-adolescence and outstripped more cooperative inclinations in virtually all experimental contexts. Previous work may have over-estimated children’s pro-social tendencie

Community cooperation and social solidarity: a case study of community initiated strategic planning

This research explored the process of creating a shared future and the evolution of cooperative collective endeavours in a regional rural community through a bottom-up planning process that involved professionals, public leadership and residents of a rural region in Israel. Using the MT rural region in Israel as a case study, the research was an interpretive exploration of how this community changed the way it collectively functions to achieve individual and shared aspirations. It examined how the community restructured its patterns of interaction, changing the social dynamics – which people interacted with each other, how they interacted with each other, and who felt committed to whom. The motivation for this inquiry stemmed from my desire as a practitioner to better understand the processes by which communities learn to function cooperatively. What are the elements that contributed to enabling a community to create the conditions for collectively utilizing and sustaining common resources rather than dividing them up for private consumption and exploitative narrow interests? What type of cooperative mechanisms enabled people to accomplish together what they cannot accomplish alone? Specifically, there are three research questions: how the change process was initiated in MT, what was significant in the nature of participation in the planning process, and how the mechanisms for regional community cooperation evolved. It was a case study of the planning and development process that I facilitated in MT from 1994-1999 (prior to my intention to undertake research) and is based mainly upon recent interviews of the participants (in that process), their recollections, and retrospective interpretations of that experience. The case has been explored from the theoretical perspective of viewing society in general, and community life in particular, as processes of constructing shared social realities that produce certain collective behaviours of cooperation or non-cooperation (Berger and Luckmann, 1967). This research was about understanding the process of making social rules that incorporate shared meanings and sanctions (Giddens, 1986) for undertaking joint endeavours (Ostrom, 1990, 1992, Wenger, 1998). Specifically two primary insights have come out of this case analysis: 1. In the MT case there was a mutually reinforcing three-way interplay between the strengthening of commitments to mutual care on the regional level, the instrumental benefits from cooperative/joint endeavours, and the envisioning of a shared future. 2. The community development process was owned by the community (not by outside agencies) and they (the community members) set the rules for community involvement. They structured the social interactions which formed the basis for creating shared understandings as a collective to achieve their common future. These insights shed light on how a community's structuring of its interactions and development interventions influenced its ability to act in a collectively optimal manner. By looking at the interrelation between trust as a function of social esteem (Honneth, 1995) and risk taking linked to instrumental benefits of cooperation (Lewis, 2002; Taylor, 1976; White, 2003) we can better understand what contributes to the way some communities continue to miss opportunities (Ostrom 1992), while others are able to promote their collective development and mutual wellbeing. By examining the process of designing (not only the design itself) community development programmes (Block, 2009) and by observing participation not as technique but as an inherent part of the way a community begins structuring its social interactions with their tacit (Polanyi, 1966) and explicit meanings, we can better understand the role of practitioners. And finally, perhaps the elements of chance and opportunity that bring certain combinations of people together in a given time and space may need to be given more weight in what remains a very unpredictable non-linear field of professional practice

Exploring Hopes And Fears From Supply Chain Innovations: An Analysis Of Antecedents And Consequences Of Supply Chain Knowledge Exchanges

This dissertation sheds light on severalhopes and fears from supply chain innovation in three distinct papers. Paper one introduces the concept of Process Innovation Propagation as an appropriation technique helping to extract the most returns out of a process innovation by exporting to supply chain partners. Paper two devises and empirically tests knowledge properties that best lead to radical and incremental supply chain innovative capabilities. Lastly, paper three conducts an exploratory study that introduces factors affecting a firm’s optimum supply chain innovation strategy. The dissertation makes a strong argument that supply chain innovation is most prominently governed by power asymmetry that may either help or hurt innovative performance

Trust in Robots

Robots are increasingly becoming prevalent in our daily lives within our living or working spaces. We hope that robots will take up tedious, mundane or dirty chores and make our lives more comfortable, easy and enjoyable by providing companionship and care. However, robots may pose a threat to human privacy, safety and autonomy; therefore, it is necessary to have constant control over the developing technology to ensure the benevolent intentions and safety of autonomous systems. Building trust in (autonomous) robotic systems is thus necessary. The title of this book highlights this challenge: “Trust in robots—Trusting robots”. Herein, various notions and research areas associated with robots are unified. The theme “Trust in robots” addresses the development of technology that is trustworthy for users; “Trusting robots” focuses on building a trusting relationship with robots, furthering previous research. These themes and topics are at the core of the PhD program “Trust Robots” at TU Wien, Austria