Database Semantics

For long-term upscaling, the computational reconstruction of a complex natural mechanism must be input-output equivalent with the prototype, i.e. the reconstruction must take the same input and produce the same output in the same processing order as the original. Accordingly, the modeling of natural language communication in Database Semantics (DBS) uses a time-linear derivation order for the speaker’s output and the hearer’s input. The language-dependent surfaces serving as the vehicle of content transfer from speaker to hearer are raw data without meaning or any grammatical properties whatsoever, but measurable by natural science.

SURVEI METODE PENGUKURAN APLIKASI CHATBOT BERBASIS MEDIA SOSIAL

The design of Chatbot aims to facilitate social activities in all areas to be considered. Chatbot is one type of machine that can communicate with humans using natural language. Communication happening via chat is a written conversation. Chatbot is a form of application implementation from Natural Language Processing (NLP) that belongs to one branch of artificial intelligence or Artificial Intelligent (AI). Social Media now provides a service that allows developers to process and integrate chatbot applications. This paper aims to review the papers that build chatbot applications in various social media using various testing methods. The contribution of this paper is to determine which method is able to measure the level of chatbot accuracy well. This review paper will choose the equation of the most widely used test methods and social media from various papers so that further research is expected to implement the right testing methods and use better social media in terms of user experience, features, and services. The review paper shows that the Black-box and System Usability Scale testing methods are most used in the review paper. This testing method is a type of method that performs testing of the flow and how the chatbot works to achieve functional validation throughly.

REFERENCES OF PROPER NAMES AS THE PROBLEM OF CONTEMPORARY PHILOSOPHY OF LANGUAGE

This article investigates the idea that meanings of proper names are their references which is popular in the philosophy of language. The aim is to show, first, that there is no satisfactory answer to the question “How references as stable relations between words and objects appear, due to accomplishment of what conditions these properties of linguistic expressions may be produced?”, and, second, that we can still use the notion of reference in our explanations of some effects of communication if we treat reference as pragmatic rather than semantic phenomenon. The actuality of this research is provided by the fact that the identification of meanings of certain types of terms, proper names first of all, with their references is still very influential account in the philosophy of language. The author uses the methods of historical exposition and philosophical analysis of the main theories of reference, such as theory of descriptions and causal theory of reference. It is shown that these theories in their different modifications fail to explain how references as semantic relations between proper names and their bearers may be produced in the course of communication and social interaction. But although there are alternative concepts of the nature meanings of proper names it is concluded that we still may apply the notion of reference in our explanations of natural language communication if we treat reference as pragmatic effect caused by mutual coordination of actions achieved by the participants of certain communicative situation.

Teaching semantics and skills for human-robot collaboration

Recent advances in robotics allow for collaboration between humans and machines in performing tasks at home or in industrial settings without harming the life of the user. While humans can easily adapt to each other and work in team, it is not as trivial for robots. In their case, interaction skills typically come at the cost of extensive programming and teaching. Besides, understanding the semantics of a task is necessary to work efficiently and react to changes in the task execution process. As a result, in order to achieve seamless collaboration, appropriate reasoning, learning skills and interaction capabilities are needed. For us humans, a cornerstone of our communication is language that we use to teach, coordinate and communicate. In this paper we thus propose a system allowing (i) to teach new action semantics based on the already available knowledge and (ii) to use natural language communication to resolve ambiguities that could arise while giving commands to the robot. Reasoning then allows new skills to be performed either autonomously or in collaboration with a human. Teaching occurs through a web application and motions are learned with physical demonstration of the robotic arm. We demonstrate the utility of our system in two scenarios and reflect upon the challenges that it introduces.

Language to Action: Towards Interactive Task Learning with Physical Agents

Language communication plays an important role in human learning and knowledge acquisition. With the emergence of a new generation of cognitive robots, empowering these robots to learn directly from human partners becomes increasingly important. This paper gives a brief introduction to interactive task learning where humans can teach physical agents new tasks through natural language communication and action demonstration. It discusses research challenges and opportunities in language and communication grounding that are critical in this process. It further highlights the importance of commonsense knowledge, particularly the very basic physical causality knowledge, in grounding language to perception and action.

Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents

We investigate the task of learning to interpret natural language instructions by jointly reasoning with visual observations and language inputs. Unlike current methods which start with learning from demonstrations (LfD) and then use reinforcement learning (RL) to fine-tune the model parameters, we propose a novel policy optimization algorithm which can dynamically schedule demonstration learning and RL. The proposed training paradigm provides efficient exploration and generalization beyond existing methods. Comparing to existing ensemble models, the best single model based on our proposed method tremendously decreases the execution error by 55% on a block-world environment. To further illustrate the exploration strategy of our RL algorithm, our paper includes systematic studies on the evolution of policy entropy during training.

Utterance and Objective: Issues in Natural Language Communication

Two premises, reflected in the title, underlie the perspective from which I will consider research in natural language processing in this article. First, progress on building computer systems that process natural languages in any meaningful sense (i.e., systems that interact reasonably with people in natural language) requires considering language as part of a larger communicative situation. Second, as the phrase “utterance and objective” suggests, regarding language as communication requires consideration of what is said literally, what is intended, and the relationship between the two.