The human capability to reason about one domain by using knowledge of other domains has been researched for more than 50 years, but models that are formally sound and predict cognitive process are sparse. We propose a formally sound method that models associative reasoning by adapting logical reasoning mechanisms. In particular it is shown that the combination with large commensense knowledge within a single reasoning system demands for an efficient and powerful association technique. This approach is also used for modelling mind-wandering and the Remote Associates Test (RAT) for testing creativity. In a general discussion we show implications of the model for a broad variety of cognitive phenomena including consciousness.
University of Cincinnati（辛辛那提大学）；Purdue University（普渡大学）
Evidential reasoning is cast as the problem of simplifying the evidence-hypothesis relation and constructing combination formulas that possess certain testable properties. Important classes of evidence as identifiers, annihilators, and idempotents and their roles in determining binary operations on intervals of reals are discussed. The appropriate way of constructing formulas for combining evidence and their limitations, for instance, in robustness, are presented.
Many important NLP problems can be posed as dual-sequence or sequence-to-sequence modeling tasks. Recent advances in building end-to-end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual-sequence modeling that is based on associative memory. We derive AM-RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN). This architecture is extended to the Dual AM-RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis…
Beyond "Hallucinations": A Framework for Stable Human-AI Reasoning
Faculty of Medicine, Lund University（隆德大学医学院）；Department of Economics, Lund University School of Economics and Management（隆德大学经济学与管理学院经济系）；Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine（伦敦大学卫生与热带医学学院健康服务研究与政策系）
Indian Statistical Institute（印度统计研究所）；Jadavpur University（贾达普大学）
To deal with uncertainty in reasoning, interval-valued logic has been developed. But uniform intervals cannot capture the difference in degrees of belief for different values in the interval. To salvage the problem triangular and trapezoidal fuzzy numbers are used as the set of truth values along with traditional intervals. Preorder-based truth and knowledge ordering are defined over the set of fuzzy numbers defined over $[0,1]$. Based on this enhanced set of epistemic states, an answer set framework is developed, with properly defined logical connectives. This type of framework is efficient i…
To improve the effectiveness of the fuzzy identification, a structure identification method based on moving rate is proposed for T-S fuzzy model. The proposed method is called "T-S modeling (or T-S fuzzy identification method) based on moving rate". First, to improve the shortcomings of existing fuzzy reasoning methods based on matching degree, the moving rates for s-type, z-type and trapezoidal membership functions of T-S fuzzy model were defined. Then, the differences between proposed moving rate and existing matching degree were explained. Next, the identification method based on moving rat…
Conversational machine comprehension requires deep understanding of the dialogue flow, and the prior work proposed FlowQA to implicitly model the context representations in reasoning for better understanding. This paper proposes to explicitly model the information gain through dialogue reasoning in order to allow the model to focus on more informative cues. The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and sequential instruction understanding dataset SCONE, which shows the effectiveness of the proposed mechanism and demonstrates its capability of …
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Zhongguancun Academy（中关村学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Shenzhen Loop Area Institute（深圳河套学院）；Tsinghua University（清华大学）
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKGs, i.e., inferring new facts from time-evolving KGs, is crucial for many applications to provide intelligent services. However, despite the prevalence of real-world data that can be represented as TKGs, most methods focus on reasoning over static knowledge graphs, or cannot predict future events. In this paper, we present a problem formulation that unifies the two major problems that need to be a…
Diagnostic reasoning has been characterized logically as consistency-based reasoning or abductive reasoning. Previous analyses in the literature have shown, on the one hand, that choosing the (in general more restrictive) abductive definition may be appropriate or not, depending on the content of the knowledge base [Console&Torasso91], and, on the other hand, that, depending on the choice of the definition the same knowledge should be expressed in different form [Poole94]. Since in Model-Based Diagnosis a major problem is finding the right way of abstracting the behavior of the system to be mo…
Conventional face-to-face classrooms are still the main learning system applied in Indonesia. In assisting such conventional learning towards an optimal learning, formative evaluations are needed to monitor the progress of the class. This task can be very hard when the size of the class is large. Hence, this research attempted to create a classroom monitoring system based on student data of Department of Electrical Engineering and Information Technology. In order to achieve the goal, a student modeling using Case-Based Reasoning was proposed. A generic student model based on a framework was de…
Carnegie Mellon University（卡内基梅隆大学）；Allen Institute for Artificial Intelligence（艾伦人工智能研究院）
Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a mental model of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our…
CETINIA Universidad Rey Juan Carlos（胡安卡洛斯国王大学CETINIA）；IMDEA Software Institute（IMDEA软件研究所）；Universidad Politécnica de Madrid（马德里理工大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Pre…
Research Center for Social Computing and Information Retrieval（社会计算与信息检索研究中心）；Harbin Institute of Technology（哈尔滨工业大学）
Understanding event and event-centered commonsense reasoning are crucial for natural language processing (NLP). Given an observed event, it is trivial for human to infer its intents and effects, while this type of If-Then reasoning still remains challenging for NLP systems. To facilitate this, a If-Then commonsense reasoning dataset Atomic is proposed, together with an RNN-based Seq2Seq model to conduct such reasoning. However, two fundamental problems still need to be addressed: first, the intents of an event may be multiple, while the generations of RNN-based Seq2Seq models are always semant…
Causal induction, i.e., identifying unobservable mechanisms that lead to the observable relations among variables, has played a pivotal role in modern scientific discovery, especially in scenarios with only sparse and limited data. Humans, even young toddlers, can induce causal relationships surprisingly well in various settings despite its notorious difficulty. However, in contrast to the commonplace trait of human cognition is the lack of a diagnostic benchmark to measure causal induction for modern Artificial Intelligence (AI) systems. Therefore, in this work, we introduce the Abstract Caus…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California, San Diego（加州大学圣地亚哥分校）；Texas A&M University（德克萨斯A&M大学）；Stevens Institute of Technology（史蒂文斯理工学院）
CALM Before the STORM: Unlocking Native Reasoning for Optimization Modeling
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Qwen Team, Alibaba Inc.（阿里巴巴集团文勤团队）；Shanghai University of Finance and Economics（上海财经大学）；Southern University of Science and Technology（南方科技大学）；Shenzhen Loop Area Institute (SLAI)（深圳环湖研究院）
National University of Singapore（新加坡国立大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究院）；New York University（纽约大学）；University of California Santa Cruz（加利福尼亚大学圣克鲁兹分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by infant psychology, researchers are now evaluating a model's ability to label scenes as either expected or surprising with knowledge of only expected scenes. However, existing VoE-based 3D datasets in physical reasoning provide mainly vision data with little to no heuristics or inductive biases. Cognitive models of physical reasoning reveal infants create high-level abstract representations of objects and interactions. …
Hulu Innovation Lab（Hulu创新实验室）；Peking University（北京大学）
Real-world scenarios demand reasoning about process, more than final outcome prediction, to discover latent causal chains and better understand complex systems. It requires the learning algorithms to offer both accurate predictions and clear interpretations. We design a set of trajectory reasoning tasks on graphs with only the source and the destination observed. We present the attention flow mechanism to explicitly model the reasoning process, leveraging the relational inductive biases by basing our models on graph networks. We study the way attention flow can effectively act on the underlyin…
Beyond Correctness: Confidence-Aware Reward Modeling for Enhancing Large Language Model Reasoning
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University（上海数据科学关键实验室，计算机科学学院，复旦大学）
CoT-Kinetics: A Theoretical Modeling Assessing LRM Reasoning Process
Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Munich Research Center, Huawei Technologies（华为技术有限公司慕尼黑研究中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Reasoning is Periodicity? Improving Large Language Models Through Effective Periodicity Modeling
School of Computer Science, Peking University（北京大学计算机学院）；aiXcoder；The Hong Kong University of Science and Technology（香港科学与技术大学）；Advanced Institute of Big Data（大数据高级研究院）
