As artificial intelligence (AI) continues advancing, ensuring positive societal impacts becomes critical, especially as AI systems become increasingly ubiquitous in various aspects of life. However, developing "AI for good" poses substantial challenges around aligning systems with complex human values. Presently, we lack mature methods for addressing these challenges. This article presents and evaluates the Positive AI design method aimed at addressing this gap. The method provides a human-centered process to translate wellbeing aspirations into concrete practices. First, we explain the method's four key steps: contextualizing, operationalizing, optimizing, and implementing wellbeing supported by continuous measurement for feedback cycles. We then present a multiple case study where novice designers applied the method, revealing strengths and weaknesses related to efficacy and usability. Next, an expert evaluation study assessed the quality of the resulting concepts, rating them moderately high for feasibility, desirability, and plausibility of achieving intended wellbeing benefits. Together, these studies provide preliminary validation of the method's ability to improve AI design, while surfacing areas needing refinement like developing support for complex steps. Proposed adaptations such as examples and evaluation heuristics could address weaknesses. Further research should examine sustained application over multiple projects. This human-centered approach shows promise for realizing the vision of 'AI for Wellbeing' that does not just avoid harm, but actively benefits humanity.
Polycraft World AI Lab (PAL): An Extensible Platform for Evaluating Artificial Intelligence Agents
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；Georgia Institute of Technology and Emory University（佐治亚理工学院和埃默里大学）
随着人工智能研究的进步，用于评估人工智能智能体的平台需要适应和发展，以继续挑战它们。我们介绍了Polycraft World AI Lab（PAL），这是一个基于Minecraft mod Polycraft World的API任务模拟器。我们的平台旨在允许具有不同架构的人工智能智能体轻松地与Minecraft世界进行交互，训练并在多个任务中进行评估。PAL允许以灵活的方式创建任务，并能够在评估期间操纵任务的任何方面。人工智能智能体和外部参与者（非玩家角色，NPC）在开放世界环境中采取的所有行动都被记录下来，以简化评估。在这里，我们提出了两个自定义任务的PAL平台上，一个专注于多步规划和一个专注于导航，并评估代理解决这些问题。总之，我们报告了一个多功能和可扩展的人工智能评估平台，人工智能研究人员可以利用它的低门槛。
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence
Naval Research Laboratory（海军研究实验室）；Knexus Research Corporation（奈克瑟斯研究公司）
Dungeon Crawl Stone Soup是一款流行的单人游戏，免费和开源的Rogue类视频游戏，具有足够复杂的决策空间，使其成为认知系统研究的理想测试平台，更广泛地说，人工智能。本文介绍了地下城爬行石头汤的属性，有利于评估人工智能系统的新方法。我们还强调了为AI研究人员构建API的持续努力，这些API体现了最近的游戏API，如MALMO，ELF和Starcraft II API。Dungeon Crawl Stone Soup的复杂性为评估AI和认知系统提供了重要的机会，包括人类用户研究。在本文中，我们提供了（1）地下城爬行石头汤的状态空间的描述，（2）我们的API组件的描述，以及（3）在地下城爬行石头汤视频游戏中评估人工智能智能体的潜在好处。
EmotionQueen: A Benchmark for Evaluating Empathy of Large Language Models
Fudan University（复旦大学）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Beijing Yuandian Technology（北京原点科技）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）
Evaluating Cognitive Age Alignment in Interactive AI Agents
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Shenzhen Children's Hospital（深圳儿童医院）；Peking University（北京大学）；Hong Kong Polytechnic University（香港理工大学）
Evaluating Multimodal Large Language Models with Daily Composite Tasks in Home Environments
State Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence（通用人工智能国家重点实验室、北京通用人工智能研究院）；School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Key Laboratory of Machine Perception (Ministry of Education), Peking University（心理与认知科学学院及北京行为与心理健康重点实验室、机器感知重点实验室（教育部））；School of Intelligence Science and Technology, Peking University（智能科学与技术学院）
MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）
Explainable Artificial Intelligence Model for Evaluating Shear Strength Parameters of Municipal Solid Waste Across Diverse Compositional Profiles
The Joint Graduate School of Energy and Environment, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区能源与环境联合研究生院）；AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi（泰国国王科技大学吞武里校区工程学院土木工程系人工智能研究组）；Center of Excellence on Energy Technology and Environment (CEE), PERDO, Ministry of Higher Education, Science, Research and Innovation (MHESI)（高等教育、科学、研究与创新部PERDO能源技术与环境卓越中心）；Department of Environmental Engineering, Faculty of Engineering, Kasetsart University（农业大学工程学院环境工程系）
Empowering Local Communities Using Artificial Intelligence
Delft University of Technology（代尔夫特理工大学）；Pennsylvania State University（宾夕法尼亚州立大学）；Carnegie Mellon University（卡内基梅隆大学）；Amsterdam Institute for Advanced Metropolitan Solutions（阿姆斯特丹高级城市解决方案研究所）
