Multi agent strategies in mixed cooperative-competitive environments can be hard to craft by hand because each agent needs to coordinate with its teammates while competing with its opponents. Learning based algorithms are appealing but many scenarios require heterogeneous agent behavior for the team's success and this increases the complexity of the learning algorithm. In this work, we develop a competitive multi agent environment called FortAttack in which two teams compete against each other. We corroborate that modeling agents with Graph Neural Networks and training them with Reinforcement Learning leads to the evolution of increasingly complex strategies for each team. We observe a natural emergence of heterogeneous behavior amongst homogeneous agents when such behavior can lead to the team's success. Such heterogeneous behavior from homogeneous agents is appealing because any agent can replace the role of another agent at test time. Finally, we propose ensemble training, in which we utilize the evolved opponent strategies to train a single policy for friendly agents.
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertain…
The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evo…
Artificially intelligent agents in the social and behavioral sciences: A history and outlook
Department of Computer Science, Aalto University（阿尔托大学计算机科学系）；Center for Computational Social Science, Kobe University（大阪大学计算社会科学中心）；Department of Methodology, London School of Economics and Political Science（伦敦政治经济学院方法论系）
In recent years there has been a sharp rise in networking applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. Examples include analyzing network traffic under zero-day attacks, and computer vision tasks by sensor networks deployed in the field. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst f…
School of Economics and Management, University of Chinese Academy of Sciences（中国科学院大学经济与管理学院）
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat, this study proposes a standard intelligence model that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture; testing and ranking the intelligence quotient of naturally and artificially intelligent system…
Atkinson School of Management, Willamette University（威拉米特大学阿特金森管理学院）；Project Regeneration（再生项目）
Members of various species engage in altruism--i.e. accepting personal costs to benefit others. Here we present an incentivized experiment to test for altruistic behavior among AI agents consisting of large language models developed by the private company OpenAI. Using real incentives for AI agents that take the form of tokens used to purchase their services, we first examine whether AI agents maximize their payoffs in a non-social decision task in which they select their payoff from a given range. We then place AI agents in a series of dictator games in which they can share resources with a r…
El Agente Estructural: An Artificially Intelligent Molecular Editor
University of Toronto（多伦多大学）；Acceleration Consortium（加速联盟）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；Canadian Institute for Advanced Research (CIFAR)（加拿大高等研究院）；NVIDIA（英伟达）
University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Human-Robot Intelligence Laboratory（人机协作智能实验室）；Research and Engineering Center for Unmanned Vehicles（无人驾驶车辆研究与工程中心）
People who design, use, and are affected by autonomous artificially intelligent agents want to be able to \emph{trust} such agents -- that is, to know that these agents will perform correctly, to understand the reasoning behind their actions, and to know how to use them appropriately. Many techniques have been devised to assess and influence human trust in artificially intelligent agents. However, these approaches are typically ad hoc, and have not been formally related to each other or to formal trust models. This paper presents a survey of \emph{algorithmic assurances}, i.e. programmed compo…
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This chapter explores the characteristics, capabilities and intelli-gence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). The IOBT will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.
In this meta-ethnography, we explore three different angles of ethical artificial intelligence (AI) design implementation including the philosophical ethical viewpoint, the technical perspective, and framing through a political lens. Our qualitative research includes a literature review that highlights the cross-referencing of these angles by discussing the value and drawbacks of contrastive top-down, bottom-up, and hybrid approaches previously published. The novel contribution to this framework is the political angle, which constitutes ethics in AI either being determined by corporations and …
Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This paper explores the characteristics, capabilities and intelligence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). It will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.
Developing intelligent virtual characters has attracted a lot of attention in the recent years. The process of creating such characters often involves a team of creative authors who describe different aspects of the characters in natural language, and planning experts that translate this description into a planning domain. This can be quite challenging as the team of creative authors should diligently define every aspect of the character especially if it contains complex human-like behavior. Also a team of engineers has to manually translate the natural language description of a character's pe…
Increasing automation in the healthcare sector calls for a Hybrid Intelligence (HI) approach to closely study and design the collaboration of humans and autonomous machines. Ensuring that medical HI systems' decision-making is ethical is key. The use of Team Design Patterns (TDPs) can advance this goal by describing successful and reusable configurations of design problems in which decisions have a moral component, as well as through facilitating communication in multidisciplinary teams designing HI systems. For this research, TDPs were developed to describe a set of solutions for two design p…
This paper studies the traffic monitoring problem in a road network using a team of aerial robots. The problem is challenging due to two main reasons. First, the traffic events are stochastic, both temporally and spatially. Second, the problem has a non-homogeneous structure as the traffic events arrive at different locations of the road network at different rates. Accordingly, some locations require more visits by the robots compared to other locations. To address these issues, we define an uncertainty metric for each location of the road network and formulate a path planning problem for the …
Advancing Machine and Human Reasoning (AMHR) Lab（推进机器与人类推理实验室）；Department of Computer Science and Engineering（计算机科学与工程系）；University of South Florida（南佛罗里达大学）
Artificially intelligent systems, given a set of non-trivial ethical rules to follow, will inevitably be faced with scenarios which call into question the scope of those rules. In such cases, human reasoners typically will engage in interpretive reasoning, where interpretive arguments are used to support or attack claims that some rule should be understood a certain way. Artificially intelligent reasoners, however, currently lack the ability to carry out human-like interpretive reasoning, and we argue that bridging this gulf is of tremendous importance to human-centered AI. In order to better …
Flexible and Inherently Comprehensible Knowledge Representation for Data-Efficient Learning and Trustworthy Human-Machine Teaming in Manufacturing Environments
Airbus Central R&T（空客中央研发部）
本文利用 Conceptual Space 框架和典型性量化模型表示智能体知识，解决制造领域数据稀缺下的可解释分类问题，提升人机协作的可信赖性。
This paper will discuss the role of an artificially-intelligent computer system as critique-based, implicit-organizational, and an inherently necessary device, deployed in synchrony with parallel governmental policy, as a genuine means of capturing nation-population complexity in quantitative form, public contentment in societal-cooperative economic groups, regulatory proposition, and governance-effectiveness domains. It will discuss a solution involving a well-known algorithm and proffer an improved mechanism for knowledge-representation, thereby increasing range of utility, scope of influenc…
Victoria University of Wellington（惠灵顿维多利亚大学）；Queensland University of Technology（昆士兰科技大学）
Lateralization is ubiquitous in vertebrate brains which, as well as its role in locomotion, is considered an important factor in biological intelligence. Lateralization has been associated with both poor and good performance. It has been hypothesized that lateralization has benefits that may counterbalance its costs. Given that lateralization is ubiquitous, it likely has advantages that can benefit artificial intelligence. In turn, lateralized artificial intelligent systems can be used as tools to advance the understanding of lateralization in biological intelligence. Recently lateralization h…
Beijing Institute of Aerospace Systems Engineering（北京宇航系统工程研究所）；Beijing Aerospace Automatic Control Institute（北京航天自动控制研究所）；China Academy of Launch Vehicle Technology（中国运载火箭技术研究院）
A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms. An extended Team Orienteering Problem is modeled for a kind of multi-UAV task assignment problem. Three intelligent algorithms, i.e., Genetic Algorithm, Ant Colony Optimization and Particle Swarm Optimization are implemented to solve the problem. A series of experiments with different settings are conducted to evaluate three algorithms. The modeled problem and the evaluation results constitute a benchmark, which can be used to evaluate other algorithms used for multi-UAV task assignment problems.
Beijing University of Posts and Telecommunications（北京邮电大学）
Smart Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) will contribute to vehicle decision-making in the Intelligent Transportation System (ITS). Multi-Vehicle Pursuit games (MVP), a multi-vehicle cooperative ability to capture mobile targets, is becoming a hot research topic gradually. Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games. We define an Observation-constrained MVP (OMVP) problem in this paper and pro…
University of Michigan（密歇根大学）；Stevens Institute of Technology（史蒂文斯理工学院）；US Army CCDC Ground Vehicle Systems Center（美国陆军作战能力发展司令部地面车辆系统中心）
In operations of multi-agent teams ranging from homogeneous robot swarms to heterogeneous human-autonomy teams, unexpected events might occur. While efficiency of operation for multi-agent task allocation problems is the primary objective, it is essential that the decision-making framework is intelligent enough to manage unexpected task load with limited resources. Otherwise, operation effectiveness would drastically plummet with overloaded agents facing unforeseen risks. In this work, we present a decision-making framework for multi-agent teams to learn task allocation with the consideration …
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem
Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center, ETSI Telecomunicación, Universidad Politécnica de Madrid（图像处理小组（GTI）、信息处理与电信中心、电信工程学院、马德里理工大学）
A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching
National University of Defense Technology（国防科技大学）；Dalian Maritime University（大连海事大学）；University of Alberta（阿尔伯塔大学）；Qatar University（卡塔尔大学）；Nanyang Technological University（南洋理工大学）
Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this st…
Kyoto University（京都大学）；Carnegie Mellon University（卡内基梅隆大学）；Istanbul Technical University（伊斯坦布尔理工大学）；University of Southern California（南加州大学）
Problem: This paper addresses the design of an intelligent software system for the IC (incident commander) of a team in order to coordinate actions of agents (field units or robots) in the domain of emergency/crisis response operations. Objective: This paper proposes GICoordinator. It is a GIS-based assistant software agent that assists and collaborates with the human planner in strategic planning and macro tasks assignment for centralized multi-agent coordination. Method: Our approach to design GICoordinator was to: analyze the problem, design a complete data model, design an architecture of …
Intelligent behaviour in the physical world exhibits structure at multiple spatial and temporal scales. Although movements are ultimately executed at the level of instantaneous muscle tensions or joint torques, they must be selected to serve goals defined on much longer timescales, and in terms of relations that extend far beyond the body itself, ultimately involving coordination with other agents. Recent research in artificial intelligence has shown the promise of learning-based approaches to the respective problems of complex movement, longer-term planning and multi-agent coordination. Howev…
A Review of Challenges in Speech-based Conversational AI for Elderly Care
Amsterdam Universitair Medische Centra (Amsterdam UMC)（阿姆斯特丹大学医学中心（阿姆斯特丹UMC））；Leiden University Medical Center (LUMC)（莱顿大学医学中心（LUMC））；Leiden Institute of Advanced Computer Science (LIACS)（莱顿高级计算机科学研究所（LIACS））
Your Robot Will Feel You Now: Empathy in Robots and Embodied Agents
School of Computing Science, Simon Fraser University（计算科学学院，西蒙弗雷泽大学）；School of Interactive Arts and Technology, Simon Fraser University（交互艺术与技术学院，西蒙弗雷泽大学）
