Introduction

the process of creating a character is iterative in nature. The following steps create an initial character that will probably need to be refined by repeating the steps as required to obtain the desired behavior.

Three actions are required to create the content necessary to drive the natural language component:

1. Create an initial set of system and user utterances. Each utterance needs to be associated to an identifying string (like a name) and the natural language understanding and dialogue management modules will use these identifiers instead of the real utterances.
2. Create a dialogue policy. This policy consists of:
   
   1. the set of variables that constitute the dialogue state (the dialogue manager is an information based one. That is it decides what to say based on what the user said and the current state of the conversation as represented in the dialogue state (also called information state)).
   2. the set of sub-dialogue networks. These sub-dialogues are similar to planning operators, with preconditions and effects. The dialogue manager (DM), will select one based on what the user said and the current dialogue state. Each sub-dialogue typically carries out a short portion of an entire dialogue (e.g. the greeting phase, or answering a question).
3. Train the natural language understanding module to map a given utterance to one of the known identifying strings defined in the first step.

The entire information that defines a character, e.g. CakeVendor, for the FLoReS system is defined in a set of files sitting in the directory resources/characters/CakeVendor/

This directory contains three sub-directories that parallel the 3 steps defined above:

• content: this directory contains the files that define the user and system utterances and their identifying strings.
• dm: this directory contains the dialogue policy
• nlu: this directory contains the natural language understanding model learned by the corresponding module from the data in the data found in the content directory.

Step 1: Authoring the content

Authoring the content consists of editing 2 files. One for the user utterances and one for the system utterances. The file that contains the user utterances is basically the training data for the natural language understanding (NLU) module. The system utterance file instead contains the utterances that the character can say.

User utterances:

The NLU module given an utterance returns the most probably identifying strings. It's based on a maximum entropy multiclass classifier and therefore the user utterance file should list utterances maintaining their natural frequency. That is, the best way to obtain these utterances is by running wizard of oz experiments or role plays. Then annotate the data by assigning to each utterance said by a user during these experiments an identifying string. These identifying strings are sometime called speech acts or dialogue acts (in case more domain specific semantic is attached to the basic speech act). Examples of dialogue acts are: question.age to mark all utterances in which the user is asking about the age of the addressee.

When we design a dialogue policy for the character using this content, whenever we want to wait for the user to say a certain utterance, we will use the string identifier (speech act) associated to that utterance.

System utterances:

These utterances are the one the system can say. Similarly to the user utterances, each utterance has a specific string identifier. When designing the dialogue policy, if we want to say a certain system utterance, we will use the corresponding identifier (also here called speech act).

File format:

The user and system utterances files use the same Excel spreadsheet format. These files have a number of columns (these are the initial 2 rows of the system utterances file for the character used in the example below):

The only 2 columns of relevance are SPEECH_ACT and TEXT.

SPEECH_ACT contains the string identifier for the corresponding utterance found in the TEXT column.

The user utterance file needs to be called user-utterances.xlsx and the system utterance file must be called system-utterances.xlsx (these names can be configured, but the default configuration looks for those names in each character available).

Step 2: Authoring the dialogue policy

Overview

The FLoReS (Forward Locking Reward Seeking) dialogue manager is an information state and event driven dialogue manager. That is it does nothing unless an event is received. When an event is received it searches for the best action (i.e. sub-dialogue) that can be executed in the current information state and that achieves the highest expected reward. Once the best action is found it start executing it. Unless:

• the current action in execution is the same as the best action found. In that case the dialogue manager simply continues to execute the action.
• there is no best action. That can happen in 2 cases:
  
  • there are no actions that can be executed given the current event and information state.
  • all executable actions have negative expected rewards

As mentioned earlier the dialogue manager searches for the best available action every time an event comes in.

Actions are also called sub-dialogues and define dialogue trees. For example this is one sub-dialogue found in the CakeVendor example below.

The policy format

The dialogue policy is composed by several files. The main file that defines it is called policy.xml (also this name can be configured, but this is the default name).

A typical policy.xml file will look like the following:

<policy xmlns:xi="http://www.w3.org/2001/XInclude">
 <xi:include href="initKB.xml"/>
 <xi:include href="goals.xml"/>
 <stepDiscount value="0.9"/>
 <include href="textFormat/policy.txt"/>
</policy>

line 2 specifies the file used to define all the variables in the information state and to initialize them.

line 3 specifies the

Step 3: Train the natural language understanding module

An example

CakeVendor.zip contains all is required to define a CakeVendor character that is an extension of the character created in this other tutorial for NPCEditor.