The Experts below are selected from a list of 8646 Experts worldwide ranked by ideXlab platform
Jingde Cheng - One of the best experts on this subject based on the ideXlab platform.
-
ACIIDS (1) - A Memory-Efficient Algorithm with Level-Order Unary Degree Sequence for Forward Reasoning Engines
Intelligent Information and Database Systems, 2018Co-Authors: Hiromu Hiidome, Yuichi Goto, Jingde ChengAbstract:A forward Reasoning Engine is an indispensable component in many advanced knowledge-based systems with purposes of creation, discovery, or prediction. Time-efficiency and memory-efficiency are crucial issues for any forward Reasoning Engine. FreeEnCal is a forward Reasoning Engine for general-purpose, and has been used for several applications, e.g., automated theorem finding. To improve time-efficiency, current implementation of FreeEnCal uses “trie”, which is a kind of tree structure, to store all logical formulas that are given or deduced in FreeEnCal. However, the implementation is not so memory-efficient from view point of applications of FreeEnCal. The paper presents a memory-efficient algorithm of FreeEnCal, and shows theoretical evaluation of the algorithm. The algorithm uses level-order unary degree sequence (LOUDS) that is a kind of succinct data structures and is used to represent tree structures concisely. By using LOUDS to construct trie in FreeEnCal, we can improve memory-efficiency of FreeEnCal.
-
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI - FreeEnCal Web: A Web Service of Automated Forward Reasoning for General-Purpose
2018 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing I, 2018Co-Authors: Takumi Otsuka, Yuichi Goto, Kentaro Fukushi, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules to given premises and obtained conclusions until some previously specified conditions are satisfied. Although a forward Reasoning Engine is an indispensable component in various advanced knowledge-based systems with purposes of creation, discovery and prediction, any forward Reasoning Engine is essentially time-consuming and memory-consuming component. A forward Reasoning Engine for general purpose, named FreeEnCal, has been proposed and developed, and can be used as a core and fundamental component in the advanced knowledge-based systems. However, it is difficult for users of FreeEnCal to prepare a computational environment that FreeEnCal is running. To solve the problem, this paper presents a Web service for automated forward Reasoning, named FreeEnCal Web. Users and programs can use FreeEnCal through Web browsers and Web API without installing FreeEnCal into own computers.
-
practical usage of freeencal an automated forward Reasoning Engine for general purpose
International Conference on Machine Learning and Cybernetics, 2012Co-Authors: Yuichi Goto, Hongbiao Gao, Takahiro Tsuji, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules. An automated forward Reasoning Engine for general-purpose, named FreeEnCal, has been proposed and developed. Then, to improving its performance and generality, fast algorithms and a general Reasoning algorithm are proposed and implemented, but separately. Until now, there is no practical implementation of FreeEnCal that are adopted those algorithms. This paper shows the practical implementation of FreeEnCal and its practical usage. We can expect to use the practical FreeEnCal for an alternative tool to prove something, an enumeration tool by using forward deduction, a web service of automated forward Reasoning.
-
ICMLC - Practical usage of freeencal: An automated forward Reasoning Engine for general-purpose
2012 International Conference on Machine Learning and Cybernetics, 2012Co-Authors: Yuichi Goto, Hongbiao Gao, Takahiro Tsuji, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules. An automated forward Reasoning Engine for general-purpose, named FreeEnCal, has been proposed and developed. Then, to improving its performance and generality, fast algorithms and a general Reasoning algorithm are proposed and implemented, but separately. Until now, there is no practical implementation of FreeEnCal that are adopted those algorithms. This paper shows the practical implementation of FreeEnCal and its practical usage. We can expect to use the practical FreeEnCal for an alternative tool to prove something, an enumeration tool by using forward deduction, a web service of automated forward Reasoning.
-
Fast Anticipatory Reasoning for Computing Anticipatory Systems
2010Co-Authors: Takahiro Koh, Yuichi Goto, Jingde ChengAbstract:Anticipatory Reasoning Engine is an indispensable component for computing anticipatory systems. In practical computing anticipatory systems, the efficiency of anticipatory Reasoning Engine is a key issue to satisfy the real-time requirements from applications. FreeEnCal: a forward Reasoning Engine for general-purpose purpose is a hopeful candidate for anticipatory Reasoning Engines. However, the current FreeEnCal is not efficient enough for practical computing anticipatory systems. This paper presents a new implementation of FreeEnCal improved by adopting fast algorithms, and shows its efficiency by comparing the improved FreeEnCal with the old one. The improved FreeEnCal can be used as an anticipatory Reasoning Engine
Simone A Ludwig - One of the best experts on this subject based on the ideXlab platform.
-
short communication comparison of a deductive database with a semantic web Reasoning Engine
Knowledge Based Systems, 2010Co-Authors: Simone A LudwigAbstract:Knowledge Engineering is a discipline concerned with constructing and maintaining knowledge bases to store knowledge of various domains and using the knowledge by automated Reasoning techniques to solve problems in domains that ordinarily require human logical Reasoning. Therefore, the two key issues in knowledge Engineering are how to construct and maintain knowledge bases, and how to reason out new knowledge from known knowledge effectively and efficiently. The objective of this paper is the comparison and evaluation of a Deductive Database system (ConceptBase) with a Semantic Web Reasoning Engine (Racer). For each system a knowledge base is implemented in such a way that a fair comparison can be achieved. Issues such as documentation, feasibility, expressiveness, complexity, distribution, performance and scalability are investigated in order to explore the advantages and shortcomings of each system.
-
performance analysis of a deductive database with a semantic web Reasoning Engine conceptbase and racer
Software Engineering and Knowledge Engineering, 2009Co-Authors: Simone A Ludwig, Craig Thompson, Kristofor AmundsonAbstract:Knowledge Engineering is a discipline concerned with constructing and maintaining knowledge bases to store knowledge of various domains and using the knowledge by automated Reasoning techniques to solve problems in domains that ordinarily require human logical Reasoning. Therefore, the two key issues in knowledge Engineering are how to construct and maintain knowledge bases, and how to reason out new knowledge from known knowledge effectively and efficiently. The objective of this paper is the evaluation of a Deductive Database system with a Semantic Web Reasoning Engine. For each system a knowledge base is implemented in such a way that comparable performance measurements can be performed. The performance and scalability are evaluated for class and instance queries.
-
SEKE - Performance Analysis of a Deductive Database with a Semantic Web Reasoning Engine: ConceptBase and Racer.
2009Co-Authors: Simone A Ludwig, Craig Thompson, Kristofor AmundsonAbstract:Knowledge Engineering is a discipline concerned with constructing and maintaining knowledge bases to store knowledge of various domains and using the knowledge by automated Reasoning techniques to solve problems in domains that ordinarily require human logical Reasoning. Therefore, the two key issues in knowledge Engineering are how to construct and maintain knowledge bases, and how to reason out new knowledge from known knowledge effectively and efficiently. The objective of this paper is the evaluation of a Deductive Database system with a Semantic Web Reasoning Engine. For each system a knowledge base is implemented in such a way that comparable performance measurements can be performed. The performance and scalability are evaluated for class and instance queries.
Yuichi Goto - One of the best experts on this subject based on the ideXlab platform.
-
ACIIDS (1) - A Memory-Efficient Algorithm with Level-Order Unary Degree Sequence for Forward Reasoning Engines
Intelligent Information and Database Systems, 2018Co-Authors: Hiromu Hiidome, Yuichi Goto, Jingde ChengAbstract:A forward Reasoning Engine is an indispensable component in many advanced knowledge-based systems with purposes of creation, discovery, or prediction. Time-efficiency and memory-efficiency are crucial issues for any forward Reasoning Engine. FreeEnCal is a forward Reasoning Engine for general-purpose, and has been used for several applications, e.g., automated theorem finding. To improve time-efficiency, current implementation of FreeEnCal uses “trie”, which is a kind of tree structure, to store all logical formulas that are given or deduced in FreeEnCal. However, the implementation is not so memory-efficient from view point of applications of FreeEnCal. The paper presents a memory-efficient algorithm of FreeEnCal, and shows theoretical evaluation of the algorithm. The algorithm uses level-order unary degree sequence (LOUDS) that is a kind of succinct data structures and is used to represent tree structures concisely. By using LOUDS to construct trie in FreeEnCal, we can improve memory-efficiency of FreeEnCal.
-
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI - FreeEnCal Web: A Web Service of Automated Forward Reasoning for General-Purpose
2018 IEEE SmartWorld Ubiquitous Intelligence & Computing Advanced & Trusted Computing Scalable Computing & Communications Cloud & Big Data Computing I, 2018Co-Authors: Takumi Otsuka, Yuichi Goto, Kentaro Fukushi, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules to given premises and obtained conclusions until some previously specified conditions are satisfied. Although a forward Reasoning Engine is an indispensable component in various advanced knowledge-based systems with purposes of creation, discovery and prediction, any forward Reasoning Engine is essentially time-consuming and memory-consuming component. A forward Reasoning Engine for general purpose, named FreeEnCal, has been proposed and developed, and can be used as a core and fundamental component in the advanced knowledge-based systems. However, it is difficult for users of FreeEnCal to prepare a computational environment that FreeEnCal is running. To solve the problem, this paper presents a Web service for automated forward Reasoning, named FreeEnCal Web. Users and programs can use FreeEnCal through Web browsers and Web API without installing FreeEnCal into own computers.
-
practical usage of freeencal an automated forward Reasoning Engine for general purpose
International Conference on Machine Learning and Cybernetics, 2012Co-Authors: Yuichi Goto, Hongbiao Gao, Takahiro Tsuji, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules. An automated forward Reasoning Engine for general-purpose, named FreeEnCal, has been proposed and developed. Then, to improving its performance and generality, fast algorithms and a general Reasoning algorithm are proposed and implemented, but separately. Until now, there is no practical implementation of FreeEnCal that are adopted those algorithms. This paper shows the practical implementation of FreeEnCal and its practical usage. We can expect to use the practical FreeEnCal for an alternative tool to prove something, an enumeration tool by using forward deduction, a web service of automated forward Reasoning.
-
ICMLC - Practical usage of freeencal: An automated forward Reasoning Engine for general-purpose
2012 International Conference on Machine Learning and Cybernetics, 2012Co-Authors: Yuichi Goto, Hongbiao Gao, Takahiro Tsuji, Jingde ChengAbstract:Forward Reasoning Engine is a computer program to automatically draw new conclusions by repeatedly applying inference rules. An automated forward Reasoning Engine for general-purpose, named FreeEnCal, has been proposed and developed. Then, to improving its performance and generality, fast algorithms and a general Reasoning algorithm are proposed and implemented, but separately. Until now, there is no practical implementation of FreeEnCal that are adopted those algorithms. This paper shows the practical implementation of FreeEnCal and its practical usage. We can expect to use the practical FreeEnCal for an alternative tool to prove something, an enumeration tool by using forward deduction, a web service of automated forward Reasoning.
-
Fast Anticipatory Reasoning for Computing Anticipatory Systems
2010Co-Authors: Takahiro Koh, Yuichi Goto, Jingde ChengAbstract:Anticipatory Reasoning Engine is an indispensable component for computing anticipatory systems. In practical computing anticipatory systems, the efficiency of anticipatory Reasoning Engine is a key issue to satisfy the real-time requirements from applications. FreeEnCal: a forward Reasoning Engine for general-purpose purpose is a hopeful candidate for anticipatory Reasoning Engines. However, the current FreeEnCal is not efficient enough for practical computing anticipatory systems. This paper presents a new implementation of FreeEnCal improved by adopting fast algorithms, and shows its efficiency by comparing the improved FreeEnCal with the old one. The improved FreeEnCal can be used as an anticipatory Reasoning Engine
T Van Allen - One of the best experts on this subject based on the ideXlab platform.
-
causal Reasoning Engine an explanation based approach to syndromic surveillance
Hawaii International Conference on System Sciences, 2005Co-Authors: B B Perry, T Van AllenAbstract:Quickly detecting an unexpected pathogen can save many lives. In cases of bioterrorism or naturally occurring epidemics, accurate diagnoses may not be made until much of the population has already been jeopardized. The goal of syndromic surveillance is to detect early anomalies that emerge from patient data in a given population area and to note disease patterns before more individuals begin to experience definitive symptoms. We developed a syndromic surveillance approach for generating advance warnings of potential wide-spread diseases as well as identifying demographic attributes that are predictive of the diseases. We describe the Causal Reasoning Engine (CRE), a multipurpose decision support system for diagnosing causes from observed symptoms and predictors. The CRE uses Bayesian inference and machine learning methods and deploys an intuitive explanation-based framework for causal modeling. We also present a diagnostic decision support tool based on the CRE that allows emergency responders to analyze and interrogate findings.
-
HICSS - Causal Reasoning Engine: An Explanation-Based Approach to Syndromic Surveillance
Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 1Co-Authors: B B Perry, T Van AllenAbstract:Quickly detecting an unexpected pathogen can save many lives. In cases of bioterrorism or naturally occurring epidemics, accurate diagnoses may not be made until much of the population has already been jeopardized. The goal of syndromic surveillance is to detect early anomalies that emerge from patient data in a given population area and to note disease patterns before more individuals begin to experience definitive symptoms. We developed a syndromic surveillance approach for generating advance warnings of potential wide-spread diseases as well as identifying demographic attributes that are predictive of the diseases. We describe the Causal Reasoning Engine (CRE), a multipurpose decision support system for diagnosing causes from observed symptoms and predictors. The CRE uses Bayesian inference and machine learning methods and deploys an intuitive explanation-based framework for causal modeling. We also present a diagnostic decision support tool based on the CRE that allows emergency responders to analyze and interrogate findings.
Sylvie Pesty - One of the best experts on this subject based on the ideXlab platform.
-
A Reasoning Module to Select ECA's Communicative Intention
2012Co-Authors: Jérémy Rivière, Carole Adam, Sylvie PestyAbstract:In the context of a ECA-human interaction, we have created a BDI-like Reasoning Engine based on the agent's mental states. This Reasoning Engine first aims to trigger the agent's emotions from its goals, beliefs, ideals and the notion of responsibility. Then this Engine selects the agent's communicative intention from its mental states and a set of dialogue rules. The integration of "Stimulus Evaluation Checks" from Scherer's appraisal theory allows us to associate the selected communicative intention with a multimodal expression. We present a test-scenario involving an argument between the user and the ECA MARC, currently used to evaluate the perceived sincerity and believability of the ECA behaviour.
-
IVA - A Reasoning module to select ECA's communicative intention
Intelligent Virtual Agents, 2012Co-Authors: Jérémy Rivière, Carole Adam, Sylvie PestyAbstract:In the context of a ECA-human interaction, we have created a BDI-like Reasoning Engine based on the agent's mental states. This Reasoning Engine first aims to trigger the agent's emotions from its goals, beliefs, ideals and the notion of responsibility. Then this Engine selects the agent's communicative intention from its mental states and a set of dialogue rules. The integration of "Stimulus Evaluation Checks" from Scherer's appraisal theory allows us to associate the selected communicative intention with a multimodal expression. We present a test-scenario involving an argument between the user and the ECA MARC, currently used to evaluate the perceived sincerity and believability of the ECA behaviour.
