Conversational Agents

Course Information
TitleΔιαλογικοί Πράκτορες / Conversational Agents
CodeIHST106
FacultySciences
SchoolInformatics
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorStavros Demetriadis
CommonNo
StatusActive
Course ID600020719

Programme of Study: PMS TECΗNOLOGIES DIADRASTIKŌN SYSTĪMATŌN (2018 éōs sīmera) PF

Registered students: 7
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses217.5
Class Information
Academic Year2024 – 2025
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
600268247
Course Type 2021
Specific Foundation
Mode of Delivery
  • Face to face
  • Distance learning
Digital Course Content
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
Prerequisites
General Prerequisites
Students who choose the course are expected to have a general education in Science/Engineering areas (e.g. from Departments of the Faculty of Sciences, Polytechnic, etc.) and to have basic programming experience (in any language) in order to understand what code and programming mean. Experience in Python programming is desirable and helps to move quickly without, however, being necessary as the instructor has prepared a special introductory MOOC in Python (in Greek) and novice students are instructed and supported to attend it.
Learning Outcomes
Upon successful completion of the course you will be able to: 1. Explain the basic concepts and processes associated with Conversational Agent (a.k.a. 'Chatbots') technology in their design, development and evaluation. 2. Explain the differences and selection criteria between Rule-Based and Machine Learning (AI-based) or AI-based technologies in chatbots development. 3. Use AIML technology to build your own chatbots. 4. Understand and apply the key aspects of 'Conversation design', which is to explain and apply design techniques for developing the conversational intelligence of a chatbot. 5. Understand the basic operating principles of machine learning and neural networking technologies for developing chatbots, with examples in environment and Python technologies. 6. Understand how machine learning tools (such as Chatterbot & Rasa) work. 7. Apply techniques for evaluating the operation of chatbots 8. Create your own chatbot suitable to be used on the company website (e-commerce) or to offer e-learning or e-health services
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Appreciate diversity and multiculturality
  • Respect natural environment
  • Demonstrate social, professional and ethical commitment and sensitivity to gender issues
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
-The course aims to offer in-depth knowledge and practical skills on a modern and highly popular technology of interaction using natural human language: the technology of Conversational Agents (or simply "Chatbots"). The content of the course is organized in sections around the following key issues: - What are chatbots and why do we need them? - What is AIML (Artificial Intelligence Markup Language) and how do we use it to build chatbots? - What are the principles of Natural Language Processing / Understanding that chatbots follow? - Which architectures for chatbots applications are most popular and what is the difference between Rule-based & AI-based chatbots? - How do we design the way a chatbot discusses and how do we evaluate its performance? - What is the use of chatbots today in the provision of e-commerce, e-health and e-education services? Design processes, development technologies, and methods for evaluating interactive agent systems will be studied, explaining the possibilities and limitations of the various approaches, and highlighting areas where conversational agents are expected to have significant further growth in the future, such as e-commerce, the provision of health services (e-health) and learning / education (e-learning). In the course we will follow a mixed approach that will combine the necessary theoretical knowledge with the practical skills of using technologies for the development of chatbots. An important part of the course is the learning of modern technological tools for the development of interactive agents, such as special tools AIML, Chatterbot, Rasa. We will also look at how to use the Python programming language and advanced specialized machine learning libraries in chatbots development. You will also hear young Greek creators talking to you who started their own startups in the field of interactive systems, conveying to you experiences, successes and reflections. A more detailed description of the course contents is available on the course page at elearning.auth.
Keywords
Conversational Agents, Chatbots, AIML, Natural Language Processing and Understanding, Chatterbot, Rasa, Python, Rule-based Design vs AI-based design
Educational Material Types
  • Notes
  • Slide presentations
  • Video lectures
  • Multimedia
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Description
Due to the content of the course (digital applications) ICT is systematically used for the presentation, education, communication and evaluation of students / three
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures27✓✓
Seminars6✓✓
Laboratory Work6✓✓✓
Reading Assigment68✓✓✓
Project75✓✓✓
Written assigments40✓✓✓
Exams3✓✓✓
Total225
Student Assessment
Student Assessment methods
  • Written Assignment (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Report (Formative, Summative)
  • Labortatory Assignment (Formative, Summative)
Last Update
21-03-2024