Welcome to this page with an overview of some of the required background information for this project. You most likely already have seen some of this information before in other courses, but some of it will also be new to you. We expect each of you to have a decent understanding of the material listed below, as that is necessary to be able to complete this project successfully. The background knowledge that we provide here is related to some of the main tools, software, and programming languages that are used within the project. There are a few quizzes that you need to complete, where the material presented below can be used to prepare (see also the Course Schedule).
Git
We use GitHub classroom to provide you with the initial agent code. GitHub is a code hosting platform for version control and collaboration. You need to join the GitHub classroom and use it for developing and sharing your code, and for storing and updating all the deliverables in this project. In order to understand how to do that, we introduce you to some basic readings and a tutorial to gain knowledge of how to use git. Git is a common tool used by many coding teams worldwide to develop code in tandem and facilitate its alignment. Getting to know git as part of this course will surely be of benefit to you in the long term.
Git Commands
You can either do just the basics reading or do the interactive tutorial, or both. There is also a more in-depth explanation of each command in the third page.
The absolute basics (reading): https://www.simplilearn.com/tutorials/git-tutorial/git-commands.
The basics (an interactive tutorial!) - https://learngitbranching.js.org/.
If you want to know more (not required): Everything on Git.
Git Merging and Conflicts
https://www.simplilearn.com/tutorials/git-tutorial/merge-conflicts-in-git
Git Best Practices
https://www.geeksforgeeks.org/best-git-practices-to-follow-in-teams/
Dialogflow
We use Google’s Dialogflow for transcribing spoken user utterances into text by means of Automatic Speech Recognition (ASR), and for classifying these texts by means of intent recognition (NLU). One of your main tasks will be designing an intent scheme and adding entities for making sense of what a user says. You will need to specify these in your Dialogflow agent, which you need to create at the start of the project (only one per team). Dialogflow is a powerful and user-friendly tool to support building a conversational agent but needs some investment on your end to get yourself acquainted with its capabilities. The resources we refer to below should provide you with some adequate references that you should read to familiarize yourself with the most important concepts used in the project.
What is a Dialogflow agent?
We talk about developing a conversational agent, but that agent itself consists of several other agents. One of those agents is a Dialogflow agent: https://cloud.google.com/dialogflow/es/docs/agents-overview.
Intents and Entities
Intents and entities are the two basic concepts from Dialogflow that we will use in this project while most other concepts will not be used. To learn about intents, read all sections until and not including ‘Rich response message’ section here:https://cloud.google.com/dialogflow/es/docs/intents-overview. To learn about entities, read until and not including ‘Session entities’ section here: https://cloud.google.com/dialogflow/es/docs/entities-overview.
Best Practices
There are a few useful practices to lean about. To do so, read until and not including ‘Protection of Consumer Practices’ section here:https://cloud.google.com/dialogflow/es/docs/agents-design. In ‘Designing for Voice’ SSML is mentioned which will not be used in this course.
An Interactive Tutorial
The best way to learn is to do. Although not required, we advise you to do thishttps://botflo.com/dialogflow-es-beginner-tutorial/.
HTML Bootstrap Library
Bootstrap is a powerful, open-source front-end framework for web development. These Bootstrap features can be used by a MARBEL agent to create and display webpages in a Chrome browser using the Social Interaction Cloud infrastructure. We here first list a few key features:
Responsive Design: Bootstrap's grid system and pre-designed components enable easy creation of responsive websites.
HTML, CSS, and JS Components: Offers a wide range of reusable components like buttons, forms, and navigation bars.
Customization: Allows extensive customization with Sass variables.
Community and Documentation: Backed by a strong community and comprehensive documentation.
Mobile-First Approach: Prioritizes mobile devices in design strategies.
This framework simplifies web development, making it accessible for beginners while still powerful for experienced developers. Here is a link to documentation for more information: https://getbootstrap.com/docs/5.3/getting-started/introduction/. Also make sure to read our Visual Support: A Guide where we explain in more detail how to use Bootstrap’s features to create webpages in this project.
Prolog
You will develop your recipe recommendation agent using MARBEL and SWI Prolog. The MARBEL agent implements a dialog management engine that you will use. You do not need to change this agent. You are, however, allowed to modify it if you like. The focus will be mostly on using Prolog to provide the agent with the knowledge it needs and to make it smarter by providing it with some logic related to its recipe recommendation task.
Prolog is a rule-based programming language based on symbolic logic. It is commonly used in Artificial Intelligence and Computational Linguistics. To understand Prolog, you should have familiarized yourself with its key concepts and structures using the book https://www.let.rug.nl/bos/lpn//lpnpage.php?pageid=online. This book covers fundamental topics like facts, rules, queries, unification, proof search, recursion, lists, arithmetic, definite clause grammars, and more. It also delves into more advanced topics such as cuts and negation. We briefly summarize here some of the core concepts for your convenience.
Logic-Based Programming: Prolog is fundamentally different from procedural languages like C or Python. It is based on formal logic, making it well-suited for tasks that involve rules and constraints, such as solving puzzles or processing natural language.
Facts, Rules, and Recursion: The core of Prolog programming involves defining facts and rules. Facts are basic statements about objects and/or their relationships. Rules define relationships between facts using basic logical relations such as conjunction, disjunction, and negation. The fact that rules can be recursive is what gives Prolog its power as a programming language. Recursion can be used, for example, for iterating over frequently used data structures in Prolog such as lists.
Lists and Arithmetic: Lists are fundamental data structures in Prolog. Prolog offers a range of built-in predicates for list manipulation. It also provides built-in support for arithmetic operations. Because Prolog’s basic form of computation is based on term matching, which does not support efficiently doing math, care must be taken to use the right operators when handling numbers in Prolog.
Pattern Matching and Unification: Prolog core form of computation consists of pattern matching with the aim of unifying Prolog terms. Unification of two terms is a fundamental operation in Prolog, which, if it succeeds, returns substitutions for Prolog variables. When these substitutions are applied to the terms (and the variables instantiated), the result would be two identical terms.
Backtracking: Prolog uses backtracking to evaluate the rules in a program to find solutions to problems. If one trace (part of a search tree) fails, Prolog automatically backtracks to find and try alternative options that have not yet been explored to continue searching for a solution.
Advanced Features: Prolog provides advanced features like the cut operator. This operator can be used for controlling the backtracking process, mainly to increase efficiency of Prolog programs.
Definite Clause Grammars (DCGs): These are used in Prolog for parsing and generating natural language constructs, making it a powerful tool for language-related applications.
Applications: Prolog is widely used in AI for tasks such as expert systems, natural language processing, and theorem proving, owing to its ability to handle complex symbolic information and logical constructs efficiently.