scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2020 ◽  
pp. 1097-1120
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael S. Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

scholarly journals Robot Task Planning in Deterministic and Probabilistic Conditions Using Semantic Knowledge Base

2016 ◽  
Vol 7 (1) ◽  
pp. 56-77 ◽  
Author(s):  
Ahmed Abdulhadi Al-Moadhen ◽  
Michael Packianather ◽  
Rossitza Setchi ◽  
Renxi Qiu
Keyword(s):  
Domain Knowledge ◽  
Semantic Knowledge ◽  
Task Planning ◽  
Relational Models ◽  
Markov Logic ◽  
Plan Generation ◽  
Knowledge Based ◽  
Probabilistic Values ◽  
Robot Task ◽  
Semantic Knowledge Base

A new method is proposed to increase the reliability of generating symbolic plans by extending the Semantic-Knowledge Based (SKB) plan generation to take into account the amount of information and uncertainty related to existing objects, their types and properties, as well as their relationships with each other. This approach constructs plans by depending on probabilistic values which are derived from learning statistical relational models such as Markov Logic Networks (MLN). An MLN module is established for probabilistic learning and inference together with semantic information to provide a basis for plausible learning and reasoning services in support of robot task-planning. The MLN module is constructed by using an algorithm to transform the knowledge stored in SKB to types, predicates and formulas which represent the main building block for this module. Following this, the semantic domain knowledge is used to derive implicit expectations of world states and the effects of the action which is nominated for insertion into the task plan. The expectations are matched with MLN output.

scholarly journals Automation in Handling Uncertainty in Semantic-knowledge based Robotic Task-planning by Using Markov Logic Networks

2014 ◽  
Vol 35 ◽  
pp. 1023-1032 ◽  
Author(s):  
Ahmed Al-Moadhen ◽  
Michael Packianather ◽  
Rossi Setchi ◽  
Renxi Qiu
Keyword(s):  
Semantic Knowledge ◽  
Task Planning ◽  
Markov Logic Networks ◽  
Markov Logic ◽  
Knowledge Based

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

scholarly journals Ontology-Driven Knowledge-Based Health-Care System, An Emerging Area – Challenges And Opportunities – Indian Scenario

2014 ◽  
Vol XL-8 ◽  
pp. 239-246 ◽  
Author(s):  
A. Sunitha ◽  
G. Suresh Babu
Keyword(s):  
Decision Making ◽  
Semantic Knowledge ◽  
Public Healthcare ◽  
Policy Makers ◽  
Knowledge Based ◽  
Healthcare Decision Making ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Decision Making System ◽  
Semantic Knowledge Base

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

Knowledge in Memetic Algorithms for Stock Classification

2014 ◽  
Vol 4 (1) ◽  
pp. 13-29 ◽  
Author(s):  
Jie Du ◽  
Roy Rada
Keyword(s):  
Evolutionary Computation ◽  
Domain Knowledge ◽  
Memetic Algorithm ◽  
Search Algorithm ◽  
Financial Statements ◽  
Memetic Algorithms ◽  
Semantic Knowledge ◽  
Optimal Solutions ◽  
Test Bed ◽  
Knowledge Based

This paper introduces a framework for a knowledge-based memetic algorithm, called KBMA. The problem of stock classification is the test bed for the performance of KBMA. Domain knowledge is incorporated into the initialization and reproduction phases of evolutionary computation. In particular, the structure of financial statements is used to sort the attributes, which contributed to a faster convergence on near optimal solutions. A semantic net is used to measure the distance between parents and offspring. Two case studies were implemented, in which domain knowledge is used to constrain the reproductive operators so that the offspring is semantically dissimilar (or similar) to the parent. The results show that KBMA outperformed the random memetic algorithm in the former case but did not in the latter case. The interpretation of the results is that when the search algorithm is distant from its goal, making large steps as defined by the semantic knowledge is helpful to the search.

Innovative Design of Manufacturing Systems

1999 ◽  
Author(s):  
Val Tsourikov ◽  
Igor Devoino
Keyword(s):  
Functional Analysis ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Qualitative Data ◽  
Semantic Knowledge ◽  
Concept Selection ◽  
Innovative Design ◽  
Knowledge Based ◽  
Semantic Knowledge Base ◽  
Innovative Concept

Abstract This paper focuses on a knowledge-based approach to innovative design of manufacturing systems and processes. Innovative design includes three major steps: Functional Analysis of the manufacturing process; Innovative Concept Search in the semantic knowledge base of technical and scientific effects and New Concept selection using quantitative as well as qualitative data.

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration
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