Carmelo Fabio Longo , Paolo Marco Riela , Daniele Francesco Santamaria , Corrado Santoro , Antonio Lieto
{"title":"基于溯演绎推理的认知聊天机器人框架","authors":"Carmelo Fabio Longo , Paolo Marco Riela , Daniele Francesco Santamaria , Corrado Santoro , Antonio Lieto","doi":"10.1016/j.cogsys.2023.05.002","DOIUrl":null,"url":null,"abstract":"<div><p><span>This paper presents a framework based on natural language processing and first-order logic aiming at instantiating </span><em>cognitive</em><span> chatbots<span>. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain<span>. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm<span> and the engine’s features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income.</span></span></span></span></p><p>The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that <span>AD-Caspar</span> is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception.</p></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"81 ","pages":"Pages 64-79"},"PeriodicalIF":2.1000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A framework for cognitive chatbots based on abductive–deductive inference\",\"authors\":\"Carmelo Fabio Longo , Paolo Marco Riela , Daniele Francesco Santamaria , Corrado Santoro , Antonio Lieto\",\"doi\":\"10.1016/j.cogsys.2023.05.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><span>This paper presents a framework based on natural language processing and first-order logic aiming at instantiating </span><em>cognitive</em><span> chatbots<span>. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain<span>. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm<span> and the engine’s features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income.</span></span></span></span></p><p>The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that <span>AD-Caspar</span> is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception.</p></div>\",\"PeriodicalId\":55242,\"journal\":{\"name\":\"Cognitive Systems Research\",\"volume\":\"81 \",\"pages\":\"Pages 64-79\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Systems Research\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1389041723000359\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Systems Research","FirstCategoryId":"102","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389041723000359","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A framework for cognitive chatbots based on abductive–deductive inference
This paper presents a framework based on natural language processing and first-order logic aiming at instantiating cognitive chatbots. The proposed framework leverages two types of knowledge bases interacting with each other in a meta-reasoning process. The first one is devoted to the reactive interactions within the environment, while the second one to conceptual reasoning. The latter exploits a combination of axioms represented with rich semantics and abduction as pre-stage of deduction, dealing also with some of the state-of-the-art issues in the natural language ontology domain. As a case study, a Telegram chatbot system has been implemented, supported by a module which automatically transforms polar and wh-questions into one or more likely assertions, so as to infer Boolean values or snippets with variable length as factoid answer. The conceptual knowledge base is organized in two layers, representing both long- and short-term memory. The knowledge transition between the two layers is achieved by leveraging both a greedy algorithm and the engine’s features of a NoSQL database, with promising timing performance if compared with the adoption of a single layer. Furthermore, the implemented chatbot only requires the knowledge base in natural language sentences, avoiding any script updates or code refactoring when new knowledge has to income.
The framework has been also evaluated as cognitive system by taking into account the state-of-the art criteria: the results show that AD-Caspar is an interesting starting point for the design of psychologically inspired cognitive systems, endowed of functional features and integrating different types of perception.
期刊介绍:
Cognitive Systems Research is dedicated to the study of human-level cognition. As such, it welcomes papers which advance the understanding, design and applications of cognitive and intelligent systems, both natural and artificial.
The journal brings together a broad community studying cognition in its many facets in vivo and in silico, across the developmental spectrum, focusing on individual capacities or on entire architectures. It aims to foster debate and integrate ideas, concepts, constructs, theories, models and techniques from across different disciplines and different perspectives on human-level cognition. The scope of interest includes the study of cognitive capacities and architectures - both brain-inspired and non-brain-inspired - and the application of cognitive systems to real-world problems as far as it offers insights relevant for the understanding of cognition.
Cognitive Systems Research therefore welcomes mature and cutting-edge research approaching cognition from a systems-oriented perspective, both theoretical and empirically-informed, in the form of original manuscripts, short communications, opinion articles, systematic reviews, and topical survey articles from the fields of Cognitive Science (including Philosophy of Cognitive Science), Artificial Intelligence/Computer Science, Cognitive Robotics, Developmental Science, Psychology, and Neuroscience and Neuromorphic Engineering. Empirical studies will be considered if they are supplemented by theoretical analyses and contributions to theory development and/or computational modelling studies.