The semantics and dynamics of `attention' are closely related to promise theoretic notions developed for autonomous agents and can thus easily be written down in promise framework. In this way one may establish a bridge between vectorized Machine Learning and Knowledge Graph representations without relying on language models implicitly. Our expectations for knowledge presume a degree of statistical stability, i.e. average invariance under repeated observation, or `trust' in the data. Both learning networks and knowledge graph representations can meaningfully coexist to preserve different aspects of data. While vectorized data are useful for probabilistic estimation, graphs preserve the intentionality of the source even under data fractionation. Using a Semantic Spacetime $γ(3,4)$ graph, one avoids complex ontologies in favour of classification of features by their roles in semantic processes. The latter favours an approach to reasoning under conditions of uncertainty. Appropriate attention to causal boundary conditions may lead to orders of magnitude compression of data required for such context determination, as required in the contexts of autonomous robotics, defence deployments, and ad hoc emergency services.
University at Buffalo（布法罗大学）；Cognotekt GmbH（科格诺泰克有限公司）；CUBRC, Inc.（CUBRC公司）
Here we understand 'intelligence' as referring to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. H…
The Belief-Desire-Intention Ontology for modelling mental reality and agency
CNR - Institute of Cognitive Sciences and Technologies（CNR认知科学与技术研究所）；CNR - Institute for Research on Population and Social Policies（CNR人口与社会政策研究所）；CNR - Research Institute on Sustainable Economic Growth（CNR可持续经济增长研究所）；University of Bologna - Department of Philosophy（博洛尼亚大学哲学系）
Vaal University of Technology（瓦尔理工大学）；North-West University（西北大学）
Electronic government (e-government) has been one of the most active areas of ontology development during the past six years. In e-government, ontologies are being used to describe and specify e-government services (e-services) because they enable easy composition, matching, mapping and merging of various e-government services. More importantly, they also facilitate the semantic integration and interoperability of e-government services. However, it is still unclear in the current literature how an existing ontology building methodology can be applied to develop semantic ontology models in a go…
University of Cambridge（剑桥大学）；Carleton University（卡尔顿大学）
Applied ontology is a relatively new field which aims to apply theories and methods from diverse disciplines such as philosophy, cognitive science, linguistics and formal logics to perform or improve domain-specific tasks. To support the development of effective research methodologies for applied ontology, we critically discuss the question how its research results should be evaluated. We propose that results in applied ontology must be evaluated within their domain of application, based on some ontology-based task within the domain, and discuss quantitative measures which would facilitate the…
Novosibirsk State University（新西伯利亚国立大学）；Sberbank of Russia（俄罗斯联邦储蓄银行）
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical …
Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents…
Imperial College London（伦敦帝国学院）；University of California, Santa Cruz（加州大学圣克鲁兹分校）
We propose an ontology of building decision-making systems, with the aim of establishing Meta-Decision-Making for Artificial Intelligence (AI), improving autonomy, and creating a framework to build metrics and benchmarks upon. To this end, we propose the three parts of Relevance, Representation, and Reasoning, and discuss their value in ensuring safety and mitigating risk in the context of third wave cognitive systems. Our nomenclature reflects the literature on decision-making, and our ontology allows researchers that adopt it to frame their work in relation to one or more of these parts.
Novosibirsk State University（新西伯利亚国立大学）；Sberbank of Russia（俄罗斯联邦储蓄银行）
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical …
Actionable Cognitive Twins are the next generation Digital Twins enhanced with cognitive capabilities through a knowledge graph and artificial intelligence models that provide insights and decision-making options to the users. The knowledge graph describes the domain-specific knowledge regarding entities and interrelationships related to a manufacturing setting. It also contains information on possible decision-making options that can assist decision-makers, such as planners or logisticians. In this paper, we propose a knowledge graph modeling approach to construct actionable cognitive twins f…
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney（澳大利亚人工智能研究所，工程与信息技术学院，悉尼技术大学）；School of Information Technology and Electrical Engineering, The University of Queensland（信息科技与电气工程学院，昆士兰大学）；Department of Data Science and AI, The Hong Kong Polytechnic University（数据科学与人工智能学院，香港理工大学）；Health Economics and Research Division, Department of Health and Aged Care（健康经济学与研究部，健康与老龄化部）
Schulich School of Business, York University（约克大学舒立克商学院）；York University（约克大学）
An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain…
Cyber defense is reactive and slow. On average, the time-to-remedy is hundreds of times larger than the time-to-compromise. In response to the expanding ever-more-complex threat landscape, Digital Twins (DTs) and particularly Human Digital Twins (HDTs) offer the capability of running massive simulations across multiple knowledge domains. Simulated results may offer insights into adversaries' behaviors and tactics, resulting in better proactive cyber-defense strategies. For the first time, this paper solidifies the vision of DTs and HDTs for cybersecurity via the Cybonto conceptual framework pr…
Cell-ontology guided transcriptome foundation model
Mila - Québec AI Institute（米拉-魁北克人工智能研究所）；University of Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Cornell University（康奈尔大学）；HEC Montréal（蒙特利尔高等商学院）；CIFAR AI Chair（加拿大高级研究所人工智能主席职位）
A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail. The review covers underlying philosophies (patternist philosophy of mind, foundational phenomenological and logical ontology), formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems partly driven by these formalizations and philosophies. The implementation of spec…
An Ontology-Based Approach to Optimizing Geometry Problem Sets for Skill Development
Harvard University（哈佛大学）；Forome Association（Forome协会）；IDEXX Laboratories（爱德士实验室）；Russian School of Mathematics（俄罗斯数学学校）；Lexington High School（列克星敦高中）；Park Hill South High School（帕克希尔南高中）；Brookline High School（布鲁克莱恩高中）
The relatively recent adoption of Knowledge Graphs as an enabling technology in multiple high-profile artificial intelligence and cognitive applications has led to growing interest in the Semantic Web technology stack. Many semantics-related tools, however, are focused on serving experts with a deep understanding of semantic technologies. For example, triplification of relational data is available but there is no open source tool that allows a user unfamiliar with OWL/RDF to import data into a semantic triple store in an intuitive manner. Further, many tools require users to have a working und…
Even though modern service-oriented and data-oriented architectures promise to deliver loosely coupled control systems, they are inherently brittle as they commonly depend on a priori agreed interfaces and data models. At the same time, the Semantic Web and a whole set of accompanying standards and tools are emerging, advocating ontologies as the basis for knowledge exchange. In this paper we aim to identify a number of key ideas from the myriad of knowledge-based practices that can readily be implemented by control systems today. We demonstrate with a practical example (a three-channel imager…
Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -- most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli. In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances d…
Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions between quantities (e.g., sleep decreases stress), between discrete events or entities (e.g., a protein inhibits another protein's transcription), or between intentional or functional factors (e.g., hospital patients pray to relieve their pain). Extracting and representing these diverse causal relations are critical for cognitive systems that operate in …
Ontologies in Motion: A BFO-Based Approach to Knowledge Graph Construction for Motor Performance Research Data in Sports Science
FIZ Karlsruhe – Leibniz Institute for Information Infrastructure（弗里堡-卡尔斯鲁厄信息基础设施研究所）；Institute of Applied Informatics and Formal Description Methods (AIFB) of KIT（卡尔斯鲁厄理工学院应用信息学与形式描述方法研究所）；Institute of Sports and Sports Science (IfSS) of KIT（卡尔斯鲁厄理工学院体育与体育科学研究所）
We propose a deep semantic characterization of space and motion categorically from the viewpoint of grounding embodied human-object interactions. Our key focus is on an ontological model that would be adept to formalisation from the viewpoint of commonsense knowledge representation, relational learning, and qualitative reasoning about space and motion in cognitive robotics settings. We demonstrate key aspects of the space & motion ontology and its formalization as a representational framework in the backdrop of select examples from a dataset of everyday activities. Furthermore, focussing on hu…
Transparency, user trust, and human comprehension are popular ethical motivations for interpretable machine learning. In support of these goals, researchers evaluate model explanation performance using humans and real world applications. This alone presents a challenge in many areas of artificial intelligence. In this position paper, we propose a distinction between descriptive and persuasive explanations. We discuss reasoning suggesting that functional interpretability may be correlated with cognitive function and user preferences. If this is indeed the case, evaluation and optimization using…
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）
Closer to Language than Steam: AI as the Cognitive Engine of a New Productivity Revolution
Department of Computer Science and Engineering（计算机科学与工程系）；University of Colorado Denver (CU Denver)（科罗拉多大学丹佛分校）；Department of Robotics and Mechatronics（机器人与机电系）；Kennesaw State University (KSU)（凯斯韦尔州立大学）
Autonomous systems with cognitive features are on their way into the market. Within complex environments, they promise to implement complex and goal oriented behavior even in a safety related context. This behavior is based on a certain level of situational awareness (perception) and advanced de-cision making (deliberation). These systems in many cases are driven by artificial intelligence (e.g. neural networks). The problem with such complex systems and with using AI technology is that there is no generally accepted approach to ensure trustworthiness. This paper presents a framework to exactl…
EHESS（高等社会科学研究院）；ENS（高等师范学校）；PSL Research University（巴黎文理研究大学）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）
During their first years of life, infants learn the language(s) of their environment at an amazing speed despite large cross cultural variations in amount and complexity of the available language input. Understanding this simple fact still escapes current cognitive and linguistic theories. Recently, spectacular progress in the engineering science, notably, machine learning and wearable technology, offer the promise of revolutionizing the study of cognitive development. Machine learning offers powerful learning algorithms that can achieve human-like performance on many linguistic tasks. Wearabl…
GAgent: An Adaptive Rigid-Soft Gripping Agent with Vision Language Models for Complex Lighting Environments
Yongjiang Laboratory（甬江实验室）；University of Nottingham Ningbo China（宁波诺丁汉大学）；The University of Hong Kong（香港大学）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore National Eye Center（新加坡国家眼科中心）；Byers Eye Institute, Stanford University（斯坦福大学拜尔斯眼科研究所）；National University of Singapore（新加坡国立大学）
