692 research outputs found
Evaluating Architectural Safeguards for Uncertain AI Black-Box Components
Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability
Artificial Intelligence and International Conflict in Cyberspace
This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation
Fixation probability in evolutionary dynamics on switching temporal networks
Population structure has been known to substantially affect evolutionary
dynamics. Networks that promote the spreading of fitter mutants are called
amplifiers of natural selection, and those that suppress the spreading of
fitter mutants are called suppressors. Research in the past two decades has
found various families of amplifiers while suppressors still remain somewhat
elusive. It has also been discovered that most networks are amplifiers under
the birth-death updating combined with uniform initialization, which is a
standard condition assumed widely in the literature. In the present study, we
extend the birth-death processes to temporal (i.e., time-varying) networks. For
the sake of tractability, we restrict ourselves to switching temporal networks,
in which the network structure alternates between two static networks at
constant time intervals. We show that, in a majority of cases, switching
networks are less amplifying than both of the two static networks constituting
the switching networks. Furthermore, most small switching networks are
suppressors, which contrasts to the case of static networks
Reflective Artificial Intelligence
As Artificial Intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today's AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward
Cooperative Open-ended Learning Framework for Zero-shot Coordination
Zero-shot coordination in cooperative artificial intelligence (AI) remains a
significant challenge, which means effectively coordinating with a wide range
of unseen partners. Previous algorithms have attempted to address this
challenge by optimizing fixed objectives within a population to improve
strategy or behaviour diversity. However, these approaches can result in a loss
of learning and an inability to cooperate with certain strategies within the
population, known as cooperative incompatibility. To address this issue, we
propose the Cooperative Open-ended LEarning (COLE) framework, which constructs
open-ended objectives in cooperative games with two players from the
perspective of graph theory to assess and identify the cooperative ability of
each strategy. We further specify the framework and propose a practical
algorithm that leverages knowledge from game theory and graph theory.
Furthermore, an analysis of the learning process of the algorithm shows that it
can efficiently overcome cooperative incompatibility. The experimental results
in the Overcooked game environment demonstrate that our method outperforms
current state-of-the-art methods when coordinating with different-level
partners. Our demo is available at https://sites.google.com/view/cole-2023.Comment: 15 pages with 9 pages main bod
A Behavioural Decision-Making Framework For Agent-Based Models
In the last decades, computer simulation has become one of the mainstream modelling techniques in many scientific fields. Social simulation with Agent-based Modelling (ABM) allows users to capture higher-level system properties that emerge from the interactions of lower-level subsystems. ABM is itself an area of application of Distributed Artificial Intelligence and Multiagent Systems (MAS). Despite that, researchers using ABM for social science studies do not fully benefit from the development in the field of MAS. It is mainly because the MAS architectures and frameworks are built upon cognitive and computer science foundations and principles, creating a gap in concepts and methodology between the two fields. Building agent frameworks based on behaviour theory is a promising direction to minimise this gap. It can provide a standard practice in interdisciplinary teams and facilitate better usage of MAS technological advancement in social research. From our survey, Triandis' Theory of Interpersonal Behaviour (TIB) was chosen due to its broad set of determinants and inclusion of an additive value function to calculate utility values of different outcomes. As TIB's determinants can be organised in a tree-like structure, we utilise layered architectures to formalise the agent's components. The additive function of TIB is then used to combine the utilities of different level determinants. The framework is then applied to create models for different case studies from various domains to test its ability to explain the importance of multiple behavioural aspects and environmental properties. The first case study simulates the mobility demand for Swiss households. We propose an experimental method to test and investigate the impact of core determinants in the TIB on the usage of different transportation modes. The second case study presents a novel solution to simulate trust and reputation by applying subjective logic as a metric to measure an agent's belief about the consequence(s) of action, which can be updated through feedback. The third case study investigates the possibility of simulating bounded rationality effects in an agent's decision-making scheme by limiting its capability of perceiving information. In the final study, a model is created to simulate migrants' choice of activities in centres by applying our framework in conjunction with Maslow's hierarchy of needs. The experiment can then be used to test the impact of different combinations of core determinants on the migrants' activities. Overall, the design of different components in our framework enables adaptations for various contexts, including transportation modal choice, buying a vehicle or daily activities. Most of the work can be done by changing the first-level determinants in the TIB's model based on the phenomena simulated and the available data. Several environmental properties can also be considered by extending the core components or employing other theoretical assumptions and concepts from the social study. The framework can then serve the purpose of theoretical exposition and allow the users to assess the causal link between the TIB's determinants and behaviour output. This thesis also highlights the importance of data collection and experimental design to capture better and understand different aspects of human decision-making
Multi-agent Learning For Game-theoretical Problems
Multi-agent systems are prevalent in the real world in various domains. In many multi-agent systems, interaction among agents is inevitable, and cooperation in some form is needed among agents to deal with the task at hand. We model the type of multi-agent systems where autonomous agents inhabit an environment with no global control or global knowledge, decentralized in the true sense. In particular, we consider game-theoretical problems such as the hedonic coalition formation games, matching problems, and Cournot games. We propose novel decentralized learning and multi-agent reinforcement learning approaches to train agents in learning behaviors and adapting to the environments. We use game-theoretic evaluation criteria such as optimality, stability, and resulting equilibria
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