Wyniki wyszukiwania - BazTech

1

Goal - oriented conversational bot for employment domain

Drozda Paweł, Żmijewski Tomasz, Osowski Maciej, Krasnodębska Aleksandra, Talun Arkadiusz

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2023

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nr 26(1)

111--123

EN

This paper focuses of the implementation of the goal – oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7.

2

A compact DQN model for mobile agents with collision avoidance

Kamola Mariusz

Journal of Automation Mobile Robotics and Intelligent Systems

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2023

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Vol. 17, No. 2

28--35

EN

This paper presents a complete simulation and reinforce‐ ment learning solution to train mobile agents’ strategy of route tracking and avoiding mutual collisions. The aim was to achieve such functionality with limited resources, w.r.t. model input and model size itself. The designed models prove to keep agents safely on the track. Colli‐ sion avoidance agent’s skills developed in the course of model training are primitive but rational. Small size of the model allows fast training with limited computational resources.

3

Overview of selected reinforcement learning solutions to several game theory problems

Jarosz Robert

Computer Science and Mathematical Modelling

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2022

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No. 15-16

13--22

EN

This paper collects several applications of reinforcement learning in solving some problems related to game theory. The methods were selected to possibly show variety of problems and approaches. Selections includes Thompson Sampling, Q-learning, DQN and AlphaGo Zero using Monte Carlo Tree Search algorithm. Paper attempts to show intuition behind proposed algorithms with shallow explaining of technical details. This approach aims at presenting overview of the topic without assuming deep knowledge about statistics and artificial intelligence.

PL

Artykuł gromadzi wybrane podejścia do rozwiązania problemów z teorii gier wykorzystując uczenie ze wzmocnieniem. Zastosowania zostały dobrane tak, aby przedstawić możliwie przekrojowo klasy problemów i podejścia do ich rozwiązania. W zbiorze wybranych algorytmów znalazły się: próbkowanie Thompsona, Q-learning (Q-uczenie), DQN, AlphaGo Zero. W artykule nacisk położono na przedstawienie intuicji sposobu działania algorytmów, koncentrując się na przeglądzie technologii zamiast na technicznych szczegółach.