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Git
We use GitHub classroom Classroom to provide you with the initial agent code. GitHub is acode 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
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You can either do just the basics reading or do , just the interactive tutorial, or both. There is also a more in-depth explanation of each command in on the third page. |
The absolute basics (reading): https://www.simplilearn.com/tutorials/git-tutorial/git-commands.
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We use Google’s Dialogflow for transcribing spoken user utterances into text by means of using 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 to make 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.
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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 the ‘Rich response message’ section here:https://cloud.google.com/dialogflow/es/docs/intents-overview. To learn about entities, read until and not including the ‘Session entities’ section here: https://cloud.google.com/dialogflow/es/docs/entities-overview.
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There are a few useful practices to lean learn about. To do so, read until and not including the ‘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.
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The best way to learn is to do. Although not required, we advise you to do thishttps://botflo.com/dialogflow-es-beginner-tutorial/.
HTML and Bootstrap
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You will be developing a few basic web pages to provide some basic visual support for to a user while they are engaging in a conversational is conversing with your agent. We assume you are familiar with basic forms of HTML, the markup language for developing a webpage. If not, please check out this https://www.w3schools.com/html/default.asp to get you started. On top of HTML, we use Bootstrap 4 for facilitating the development of a webpage. The main purpose of this visual support is twofold: to provide (1) support to a user to reduce their cognitive load (the amount of information working memory needs to process at any given time) and (2) an indication of the progress on the task that has been made thus far. A user may not be able to remember all preferences and/or constraints on recipes they selected thus far. A system that would require a user to do so, would likely not be experienced as very user-friendly. It is also nice to show a user how much progress has been made in finding a recipe they like. A simple measure for our recipe recommendation agent to indicate progress is to show how many recipes still match the preferences of the user.
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To understand how you can integrate Bootstrap components into your agent, you need to read our Visual Support Guide. This is where we explain in more detail how to you can use Bootstrap components for creating a webpage in this project.
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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: The 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 the efficiency of Prolog programs.
Definite Clause Grammars (DCGs): These are used in Prolog for parsing and generating natural language constructs, making it them 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.