با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Ontology-based knowledge management with verbal interaction for command interpretation and execution by home service robots
مدیریت دانش مبتنی بر هستی شناسی با تعامل کلامی برای تفسیر و اجرای فرمان توسط روبات های خدمات خانگی-2021 This paper describes a system for service robots that combines ontological knowledge reasoning and
human–robot interaction to interpret natural language commands and successfully perform household
chores, such as finding and delivering objects. Knowledge and context reasoning is essential for
providing more efficient service robots, given their diverse and continuously changing environments.
Moreover, since they are in contact with humans, robots require such skills as interaction and language.
Therefore, we developed a system with specific modules to manage robots’ knowledge and reasoning,
command analysis, decision-making, and talking interaction. The system relies on inference methods
and verbal interaction to understand commands and clarify uncertain information. We tested our
system inside a simulated environment where the robot receives commands with missing or unclear
information. The system’s performance was compared with the average performance of human subjects
who completed the same commands in the simulation.
keywords: مدیریت دانش | مبتنی بر هستی شناسی | ربات های خدمات | تعامل انسان و ربات | Knowledge management | Ontology-based | Service robots | Human–robot interaction |
مقاله انگلیسی |
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ADRL: An attention-based deep reinforcement learning framework for knowledge graph reasoning
ADRL: یک چارچوب یادگیری تقویتی عمیق مبتنی بر توجه برای استدلال نمودار دانش-2020 Knowledge graph reasoning is one of the key technologies for knowledge graph construction, which
plays an important part in application scenarios such as vertical search and intelligent question
answering. It is intended to infer the desired entity from the entities and relations that already exist in
the knowledge graph. Most current methods for reasoning, such as embedding-based methods, globally
embed all entities and relations, and then use the similarity of vectors to infer relations between
entities or whether given triples are true. However, in real application scenarios, we require a clear and
interpretable target entity as the output answer. In this paper, we propose a novel attention-based deep
reinforcement learning framework (ADRL) for learning multi-hop relational paths, which improves
the efficiency, generalization capacity, and interpretability of conventional approaches through the
structured perception of deep learning and relational reasoning of reinforcement learning. We define
the entire process of reasoning as a Markov decision process. First, we employ CNN to map the
knowledge graph to a low-dimensional space, and a message-passing mechanism to sense neighbor
entities at each level, and then employ LSTM to memorize and generate a sequence of historical
trajectories to form a policy and value functions. We design a relational module that includes a selfattention
mechanism that can infer and share the weights of neighborhood entity vectors and relation
vectors. Finally, we employ the actor–critic algorithm to optimize the entire framework. Experiments
confirm the effectiveness and efficiency of our method on several benchmark data sets. Keywords: Knowledge graph | Knowledge reasoning | Reinforcement learning | Deep learning | Attention |
مقاله انگلیسی |
3 |
A methodology for enhancing the reliability of expert system applications in probabilistic risk assessment
روشی برای افزایش قابلیت اطمینان برنامه های کاربردی سیستم خبره در ارزیابی ریسک احتمالی-2019 In highly complex industries, capturing and employing expert systems is significantly important to an organizations
success considering the advantages of knowledge-based systems. The two most important issues within
the expert system applications in risk and reliability analysis are the acquisition of domain experts professional
knowledge and the reasoning and representation of the knowledge that might be expressed. The first issue can be
correctly handled by employing a heterogeneous group of experts during the expert knowledge acquisition
processes. The members of an expert panel regularly represent different experiences and knowledge.
Subsequently, this diversity produces various sorts of information which may be known or unknown, accurate or
inaccurate, and complete or incomplete based on its cross-functional and multidisciplinary nature. The second
issue, as a promising tool for knowledge reasoning, still suffers from lack of deficiencies such as weight and
certainty factor, and are insufficient to accurately represent complex rule-based expert systems. The outputs in
current expert system applications in probabilistic risk assessment could not accurately represent the increasingly
complex knowledge-based systems. The reason is the lack of certainty and self-assurance of experts when
they are expressing their opinions. In this paper, a novel methodology is presented based on the concept of Znumbers
to overcome this issue. A case study in a high-tech process industry is provided in detail to demonstrate
the application and feasibility of the proposed methodology. Keywords: Confidence level | Z-numbers | Fault tree analysis | Spherical hydrocarbon storage tank |
مقاله انگلیسی |
4 |
Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey
تکنیک بازنویسی دانش هستی شناسی، مدل سازی دامنه زبان ها و برنامه ریزان برای برنامه ریزی مسیر رباتیک: یک مرور-2018 Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated
towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning
techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge
about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer
new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge
using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also
provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.
Keywords: Path planning; Knowledge representation; Reasoning; Ontology; Spatial; Temporal; Semantic knowledge; Planners; Modeling languages |
مقاله انگلیسی |