In this paper we suggest an architecture for a software agent which operates a physical device and is capable of making observations and of testing and repairing the device's components. We present simplified definitions of the notions of symptom, candidate diagnosis, and diagnosis which are based on the theory of action language ${\cal AL}$. The definitions allow one to give a simple account of the agent's behavior in which many of the agent's tasks are reduced to computing stable models of logic programs.
When modeling real world domains we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using the tableau algorithm, which has to handle non-determinism. Prolog, with its effic…
"Simply Logical -- Intelligent Reasoning by Example" by Peter Flach was first published by John Wiley in 1994. It could be purchased as book-only or with a 3.5 inch diskette containing the SWI-Prolog programmes printed in the book (for various operating systems). In 2007 the copyright reverted back to the author at which point the book and programmes were made freely available online; the print version is no longer distributed through John Wiley publishers. In 2015, as a pilot, we ported most of the original book into an online, interactive website using SWI-Prolog's SWISH platform. Since then…
To appear in Theory and Practice of Logic Programming (TPLP), 2008. We are researching the interaction between the rule and the ontology layers of the Semantic Web, by comparing two options: 1) using OWL and its rule extension SWRL to develop an integrated ontology/rule language, and 2) layering rules on top of an ontology with RuleML and OWL. Toward this end, we are developing the SWORIER system, which enables efficient automated reasoning on ontologies and rules, by translating all of them into Prolog and adding a set of general rules that properly capture the semantics of OWL. We have also …
University of the Bío-Bío（比奥比奥大学）；University of Dundee（邓迪大学）；Universidade de Santiago de Compostela（圣地亚哥德孔波斯特拉大学）
In this paper we analyse the benefits of incorporating interval-valued fuzzy sets into the Bousi-Prolog system. A syntax, declarative semantics and im- plementation for this extension is presented and formalised. We show, by using potential applications, that fuzzy logic programming frameworks enhanced with them can correctly work together with lexical resources and ontologies in order to improve their capabilities for knowledge representation and reasoning.
École Polytechnique de Montréal（蒙特利尔综合理工学院）；Université de Montréal（蒙特利尔大学）
When working on intelligent tutor systems designed for mathematics education and its specificities, an interesting objective is to provide relevant help to the students by anticipating their next steps. This can only be done by knowing, beforehand, the possible ways to solve a problem. Hence the need for an automated theorem prover that provide proofs as they would be written by a student. To achieve this objective, logic programming is a natural tool due to the similarity of its reasoning with a mathematical proof by inference. In this paper, we present the core ideas we used to implement suc…
Bayesian belief networks are bing increasingly used as a knowledge representation for diagnostic reasoning. One simple method for conducting diagnostic reasoning is to represent system faults and observations only. In this paper, we investigate how having intermediate nodes-nodes other than fault and observation nodes affects the diagnostic performance of a Bayesian belief network. We conducted a series of experiments on a set of real belief networks for medical diagnosis in liver and bile disease. We compared the effects on diagnostic performance of a two-level network consisting just of dise…
The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have done in this research field by presenting a probabilistic semantics for description logics and reasoning and learning algorithms. In particular, we present in detail the system TRILL P, which computes the probability of queries w.r.t. probabilistic knowledge bases, which has been…
Institute of Philosophy, University of Warsaw（华沙大学哲学研究所）
The combined approach of the Qualitative Reasoning and Probabilistic Functions for the knowledge representation is proposed. The method aims at represent uncertain, qualitative knowledge that is essential for the moving blocks task's execution. The attempt to formalize the commonsense knowledge is performed with the Situation Calculus language for reasoning and robot's beliefs representation. The method is implemented in the Prolog programming language and tested for a specific simulated scenario. In most cases the implementation enables us to solve a given task, i.e., move blocks to desired p…
Arizona State University（亚利桑那州立大学）；Texas Tech University（德克萨斯理工大学）
This paper develops a declarative language, P-log, that combines logical and probabilistic arguments in its reasoning. Answer Set Prolog is used as the logical foundation, while causal Bayes nets serve as a probabilistic foundation. We give several non-trivial examples and illustrate the use of P-log for knowledge representation and updating of knowledge. We argue that our approach to updates is more appealing than existing approaches. We give sufficiency conditions for the coherency of P-log programs and show that Bayes nets can be easily mapped to coherent P-log programs.
For the diagnostic inference under uncertainty Bayesian networks are investigated. The method is based on an adequate uniform representation of the necessary knowledge. This includes both generic and experience-based specific knowledge, which is stored in a knowledge base. For knowledge processing, a combination of the problem-solving methods of concept-based and case-based reasoning is used. Concept-based reasoning is used for the diagnosis, therapy and medication recommendation and evaluation of generic knowledge. Exceptions in the form of specific patient cases are processed by case-based r…
Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）
University of Genova（热那亚大学）；Ministry of Justice（司法部）
On May 12th, 2020, during the course entitled Artificial Intelligence and Jurisdiction Practice organized by the Italian School of Magistracy, more than 70 magistrates followed our demonstration of a Prolog logical judge reasoning on an armed robbery case. Although the implemented logical judge is just an exercise of knowledge representation and simple deductive reasoning, a practical demonstration of an automated reasoning tool to such a large audience of potential end-users represents a first and unique attempt in Italy and, to the best of our knowledge, in the international panorama. In thi…
Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural languages. In this report, we focus on a class of CNLs, called machine-oriented CNLs, which have well-defined semantics that can be deterministically translated into formal languages, such as Prolog, to do logical reasoning. Over the past 20 years, a number of machine-oriented CN…
Robots assisting humans in complex domains have to represent knowledge and reason at both the sensorimotor level and the social level. The architecture described in this paper couples the non-monotonic logical reasoning capabilities of a declarative language with probabilistic belief revision, enabling robots to represent and reason with qualitative and quantitative descriptions of knowledge and degrees of belief. Specifically, incomplete domain knowledge, including information that holds in all but a few exceptional situations, is represented as a Answer Set Prolog (ASP) program. The answer s…
Bayesian networks offer great potential for use in automating large scale diagnostic reasoning tasks. Gibbs sampling is the main technique used to perform diagnostic reasoning in large richly interconnected Bayesian networks. Unfortunately Gibbs sampling can take an excessive time to generate a representative sample. In this paper we describe and test a number of heuristic strategies for improving sampling in noisy-or Bayesian networks. The strategies include Monte Carlo Markov chain sampling techniques other than Gibbs sampling. Emphasis is put on strategies that can be implemented in distrib…
A new probabilistic network construction system, DYNASTY, is proposed for diagnostic reasoning given variables whose probabilities change over time. Diagnostic reasoning is formulated as a sequential stochastic process, and is modeled using influence diagrams. Given a set O of observations, DYNASTY creates an influence diagram in order to devise the best action given O. Sensitivity analyses are conducted to determine if the best network has been created, given the uncertainty in network parameters and topology. DYNASTY uses an equivalence class approach to provide decision thresholds for the s…
Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning
University of New South Wales（新南威尔士大学）；Essential Energy；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Zhejiang University（浙江大学）；Fudan University（复旦大学）；Tsinghua University（清华大学）
This paper discusses an expert system shell that integrates rule-based reasoning and the Dempster-Shafer evidence combination scheme. Domain knowledge is stored as rules with associated belief functions. The reasoning component uses a combination of forward and backward inferencing mechanisms to allow interaction with users in a mixed-initiative format.
Lung-R1: A Knowledge Graph-Guided LLM for Pulmonary Diagnostic Reasoning
School of Computer Science, Chongqing University（重庆大学计算机学院）；AI Research Institution, Mashang Financial Institution（马上金融人工智能研究院）；Department of Information, Third Military Medical University（陆军军医大学信息系）
Teaching AI Stepwise Diagnostic Reasoning with Report-Guided Chain-of-Thought Learning
Fujian University of Technology（福建工程学院）；Fujian Provincial Key Laboratory of Big Data Mining and Applications（福建省大数据挖掘与应用重点实验室）；Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences（生物医学成像科学与系统重点实验室，中国科学院）
Diagnostic reasoning is a key component of many professions. To improve students' diagnostic reasoning skills, educational psychologists analyse and give feedback on epistemic activities used by these students while diagnosing, in particular, hypothesis generation, evidence generation, evidence evaluation, and drawing conclusions. However, this manual analysis is highly time-consuming. We aim to enable the large-scale adoption of diagnostic reasoning analysis and feedback by automating the epistemic activity identification. We create the first corpus for this task, comprising diagnostic reason…
Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning
Stanford University（斯坦福大学）；Jordan High School（乔丹高中）；UC San Diego（加利福尼亚大学圣迭戈分校）；Winchester High School（温彻斯特高中）；James Logan High School（詹姆斯·洛根高中）；Rutgers University（罗格斯大学）；Foothill–De Anza College（福希尔-德安扎学院）；Fordham University（福特汉姆大学）；Chapman University（查普曼大学）；UC Berkeley（加利福尼亚大学伯克利分校）
ReasoningLens: Hierarchical Visualization and Diagnostic Auditing for Large Reasoning Models
Chinese Information Processing Laboratory（中国信息处理实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）
PIE is a Prolog-embedded environment for automated reasoning on the basis of first-order logic. Its main focus is on formulas, as constituents of complex formalizations that are structured through formula macros, and as outputs of reasoning tasks such as second-order quantifier elimination and Craig interpolation. It supports a workflow based on documents that intersperse macro definitions, invocations of reasoners, and LaTeX-formatted natural language text. Starting from various examples, the paper discusses features and application possibilities of PIE along with current limitations and issu…
ConsultMind:Towards Automated Diagnostic Consultation via Uncertainty-Aware Reasoning
College of Computer Science, Chongqing University（重庆大学计算机科学学院）；College of Computer Science, Hunan University（湖南大学计算机科学学院）；AI Labs, Unisound（云知声AI实验室）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Psycharity, Chongqing Medical University（重庆医科大学精神医学中心）；School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Institute of Advanced Intelligence and Computing (IAIC), Agency for Science, Technology and Research (A*STAR), Singapore（新加坡科技研究局先进智能与计算研究所）；Technological Information, Chongqing Center for Disease Control and Prevention（重庆市疾病预防控制中心科技信息处）
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models
Shanghai Jiao Tong University（上海交通大学）；SII；SPIRAL Lab（SPIRAL实验室）；Generative AI Research Lab (GAIR)（生成式AI研究实验室）；Shanghai Chest Hospital（上海胸科医院）；Beijing Anzhen Hospital, Capital Medical University（北京安贞医院，首都医科大学）
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe)（人类元宇宙医学高级研究机构（WPI-PRIMe））；Osaka University（大阪大学）；Department of Cardiology, The First Affiliated Hospital of Dalian Medical University（大连医科大学第一附属医院心内科）；Institute of Computing Technology, Chinese Academy of Science（中国科学院计算技术研究所）；D3 Center, Osaka University（大阪大学D3中心）；Agency for Science, Technology and Research (A*STAR)（科技研究局（A*STAR））
