How to Build an Intelligent Chatbot?
Building blocks of a cognitive conversational agent
Chatbots are computer programs that can have a conversational interaction with human users. By default, a chatbot need not be intelligent. What it needs to be is useful and usable. For instance, a chatbot whose task is to collect information from users, will simply ask users questions and provide them an easy touch and swipe style response mechanism using buttons and carousels. While it does the task it is designed to perform, it cannot be counted as an intelligent chatbot.
But this is not to say that Chatbots need not be intelligent at all. As the task carried out by the chatbot becomes more complex, the need for it to be intelligent increases. It is intelligence that makes a complex conversation easy and effortless. Here are three dimensions that you could pay attention to if you want your chatbot to be an intelligent one.
1. Perception
Perception is the part where the chatbot gets to know what the user wants. On platforms like Facebook Messenger, chatbots can present users with a set of buttons to get their input. This is an easy and robust approach to getting user inputs. However, this approach lacks the fluidity of human conversation. For instance, imagine a user searching for comedy shows and he is being asked to enter the date information. He is given a list and is asked to pick one. However, he would probably like say “any Wednesday in the next three weeks”. A chatbot that can understand this will be perceived as more intelligent than one that cannot.
Another example and a source of user frustration is when the user wants to say “is it available in yellow?” when offered the other options like “red” and “blue”. While the chatbot has made it clear that the product is not available in yellow by not giving him/her an option, the user might still ask the question with an expectation of getting a favourable response (e.g. the product is currently out of stock and will be available in yellow in the near future).
In order to keep the design complexity low, you could try to do this in two steps. Step 1: Keep it local. Get the meaning of the user’s utterance that are in response to the chatbot’s question and ignore proactive user utterances. This reduces the number of responses users might have in a given context and thereby reduce design and programming effort. Step 2: Once your chatbot can sense natural language locally in response to questions, try sensing NL utterances of the user when he/she is proactive and takes the initiative. There are a number of toolkits that you can use to add NL capability to your chatbot such as API.ai, IBM Watson, Wit.ai, etc.
Intelligent perception also involves understanding other forms of input like emoticons, emojis, gifs and images. Intelligent chatbot must make sense of the user’s intentions based on images and respond appropriately.
2. Learning
Another trait of intelligence that your chatbot can have is learning. Does your chatbot learn? Does it learn to improve its performance over time? Individual modules of your chatbot such as NL understanding and user modelling modules can learn to perform better over time using machine learning (ML) algorithms in tandem with human supervisors. A number of ML techniques are available: supervised, unsupervised, and reinforcement learning. Each of these can be realised using a variety of algorithms. For tasks like classifying user intents from user utterances, supervised learning can be used. For finding clusters of users based on their conversational behaviours, unsupervised clustering algorithms can be used. And for learning efficient and optimal conversation behaviours (i.e. what should the bot say now?), reinforcement learning algorithms can be used.
There are many ways to say the same thing (i.e. user intent). Let us assume that your chatbot recognises N different user intents. Each intent can be expressed in a number of ways. However, the initial version of a chatbot may not include all of them. And therefore it will fail to understand the user’s utterance even though it is capable of responding to the user intent expressed by the utterance. Such instances can be logged, annotated and fed into a ML module. By iterating over missed expressions using machine learning, your chatbot can learn to understand users better over time.
However, one crucial thing that you need to remember is that machine learning algorithms learn from data and experience that is made available to them. And therefore quality of such data and experience is very important. It would be a good idea to first collect enough data using a system that is hand crafted and then use machine learning algorithms to improve its performance. Some toolkits that you can use to perform ML tasks are Weka, Google’s TensorFlow.
3. Planning
The third dimension to intelligent behaviour is planning. Planning is an internal task done by chatbot to decide how to carry out the task the user has requested. For simple tasks like user surveys, there is not a lot of planning required. The bot needs to move on from one question to the next until it is all over. However, if the bot is supposed to to carry out a complex task then it needs to have the capability of finding the sequence of actions that will lead to goal set by the user. Such a sequence of actions is called a plan. These plans would also include conversational actions such as asking, informing, acknowledging, etc.
Currently, tasks are decomposed by developers themselves and ready-made plans are fed to chatbots. However if a chatbot is to multitask, then it should be able to come up with it own plans. This would save chatbot designers and developers the efforts to map every step in the potential conversational flow between users and chatbots.
To put this in perspective, a chatbot with planning capabilities will be able to come up with a sequence of actions to achieve the goal set by a user. So, if the user is asking to know about the status of a delayed delivery, the chatbot (working for a retail business) would be able to figure out which database or API to query. Once it has figured out where the information is, it will then figure out if it has all the required parameters to make such a query. It will question the user to get the missing parameters, query the database and return the information about the delayed delivery. More importantly, it will replan its action sequence if the user does not behave as it expected.
If a chatbot can figure out the steps leading to the goal by itself, it will simplify the development process greatly. Developers may then be able to focus on what the chatbot needs to do than how to do it, because it can figure it out for itself. This very problem has been the focus of a strand of AI research called AI Planning. There aren’t any easy to use AI planning toolkit that is available (as far as I know). However, many AI planning algorithms are available (e.g. STRIPS, GraphPlan). Until now, very little has been explored about the use of AI planning to dynamically generate with conversational plans but, in my opinion, it holds so much promise for the future of intelligent chatbots.
To conclude, using natural language understanding, machine learning and AI planning algorithms, chatbots can be made more intelligent than they already are. This is not to say that all chatbots need to be intelligent. But as chatbots gear up to face more users and more user-centric tasks, making them more intelligent to be able to handle both the tasks and the natural conversations with users is not going to be optional.
