Gossip has been shown to be a relatively efficient solution to problems of cooperation in reputation-based systems of exchange, but many studies don't conceptualize gossiping in a realistic way, often assuming near-perfect information or broadcast-like dynamics of its spread. To solve this problem, we developed an agent-based model that pairs realistic gossip processes with different variants of Trust Game. The results show that cooperators suffer when local interactions govern spread of gossip, because they cannot discriminate against defectors. Realistic gossiping increases the overall amount of resources, but is more likely to promote defection. Moreover, even partner selection through dynamic networks can lead to high payoff inequalities among agent types. Cooperators face a choice between outcompeting defectors and overall growth. By blending direct and indirect reciprocity with reputations we show that gossiping increases the efficiency of cooperation by an order of magnitude.

A.2024.0146 1 Rolling Horizon Coverage Control with Collaborative Autonomous Agents Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou and Marios M. Polycarpou Abstract This work proposes a coverage controller that enables an aerial team of distributed autonomous agents to collaboratively generate non-myopic coverage plans over a rolling finite horizon, aiming to cover specific points on the surface area of a 3D object of interest. The collaborative coverage problem, formulated, as a distributed model predictive control problem, optimizes the agents' motion and camera control inputs, while considering inter-agent constraints aiming at reducing work redundancy. The proposed coverage controller integrates constraints based on light-path propagation techniques to predict the parts of the object's surface that are visible with regard to the agents' future anticipated states. This work also demonstrates how complex, non-linear visibility assessment constraints can be converted into logical expressions that are embedded as binary constraints into a mixed-integer optimization framework. The proposed approach has been demonstrated through simulations and practical applications for inspecting buildings with unmanned aerial vehicles (UA Vs). I NTRODUCTION The interest in swarm systems such as systems utilizing multiple autonomous unmanned aerial vehicles (UA Vs) has skyrocketed over the last few decades. Rapid advancements in robotics, automation and artificial intelligence coupled with the decreasing costs of electronic components have fuelled a remarkable surge in interest towards the technologies and applications of swarming systems. This work addresses the challenge of coverage planning and control using multiple collaborative intelligent autonomous agents, specifically autonomous UA Vs. Coverage planning [1] is crucial in several application domains including search and rescue operations and critical infrastructure inspections. It is one of the essential functionalities that can notably enhance the autonomy of existing swarming systems enabling them to execute fully automated missions in the aforementioned scenarios. In coverage planning our objective is to design trajectories that allow a team of autonomous mobile agents to comprehensively cover a designated area or points of interest. Concurrently we aim to optimize a specific mission goal such as minimizing the mission's duration and energy consumption of the agents. This work introduces a coverage control framework that optimizes both the kinematic and camera control inputs of multiple UA V agents simultaneously.

Collective Intelligence plays a central role in a large variety of fields, from economics and evolutionary theory to neural networks and eusocial insects, and it is also core to much of the work on emergence and self-organisation in complex systems theory. However, in human collective intelligence there is still much more to be understood in the relationship between specific psychological processes at the individual level and the emergence of self-organised structures at the social level. Previously psychological factors have played a relatively minor role in the study of collective intelligence as the principles are often quite general and applicable to humans just as readily as insects or other agents without sophisticated psychologies. In this article we emphasise, with examples from other complex adaptive systems, the broad applicability of collective intelligence principles while the mechanisms and time-scales differ significantly between examples. We contend that flexible collective intelligence in human social settings is improved by our use of a specific cognitive tool: our Theory of Mind. We identify several key characteristics of psychologically mediated collective intelligence and show that the development of a Theory of Mind is a crucial factor distinguishing social collective intelligence from general collective intelligence. We then place these capabilities in the context of the next steps in artificial intelligence embedded in a future that includes an effective human-AI hybrid social ecology.

Modern Reinforcement Learning (RL) algorithms are able to outperform humans in a wide variety of tasks. Multi-agent reinforcement learning (MARL) settings present additional challenges, and successful cooperation in mixed-motive groups of agents depends on a delicate balancing act between individual and group objectives. Social conventions and norms, often inspired by human institutions, are used as tools for striking this balance. In this paper, we examine a fundamental, well-studied social convention that underlies cooperation in both animal and human societies: dominance hierarchies. We adapt the ethological theory of dominance hierarchies to artificial agents, borrowing the established terminology and definitions with as few amendments as possible. We demonstrate that populations of RL agents, operating without explicit programming or intrinsic rewards, can invent, learn, enforce, and transmit a dominance hierarchy to new populations. The dominance hierarchies that emerge have a similar structure to those studied in chickens, mice, fish, and other species.

Recent developments in computational physiology have effectively exploited advanced signal processing and artificial intelligence tools for uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated better-than-human diagnostic capabilities, the high complexity of these computational'black boxes' may severely limit scientific inference, especially in terms of biological insights on disease mechanisms. This theme issue highlights challenges and opportunities of advanced computational tools for processing series comprehensive of autonomic nervous system dynamics, with a more specific focus on cardiovascular control physiology and pathology. Such a wide panoramic perspective highlights the issues of specificity in heartbeat-related features and fosters the transition from the black-box paradigm to interpretable and personalised clinical models in cardiology research. This issue will soon be available to buy in print.

OpenWorm Foundation, Boston, MA USA Abstract While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks. Introduction How can Artificial Neural Networks (ANNs) emulate the "lifelike" nature of Biological Neural Networks (BNNs)?

Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Last November Chinese scientist He Jiankui announced the birth of twin babies whose germline he claimed to have altered to reduce their susceptibility to contracting HIV. The news of embryo editing and gene-edited babies prompted immediate condemnation both within and beyond the scientific community. An ABC News headline asked: "Genetically edited babies--scientific advancement or playing God?" The answer may be "both." He's application of gene-editing technology to human embryos flouted norms of scientific transparency and oversight, but even less controversial scientific developments sometimes provoke the reaction that humans are overstepping their appropriate sphere of influence.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.