Chatbots are computer programs that can converse with human users conversationally. A chatbot does not have to be intelligent by default. It must be functional and simple to use. For example, a chatbot tasked with gathering information from users will ask them questions and present them with a simple touch and swipe response mechanism in the form of buttons and carousels. While it does the goal at hand, it cannot be considered an intelligent chatbot developer.

Perception

The chatbot training perception is how it learns what the user wants. The chatbot developer can show users a series of buttons to solicit their opinions on Facebook Messenger platforms. This is a reliable and straightforward method of obtaining user input. On the other hand, this method lacks the fluidity of human conversation. Consider a user who is looking for comedy gigs and is being requested to provide date information. He’s handed a list and told to choose one. He’d presumably prefer to say “any Wednesday in the next three weeks.” A chatbot that understands this will be considered smarter than one that does not.

Even though the chatbot training has made it plain that the product is not available in yellow by not providing an option, the user may continue to ask the query with the expectation of a favourable response.

You might try to implement this in two steps to keep the design complexity low.

  • Step 1: Keep it local. Get the meaning of the user’s responses to the chatbot’s questions and disregard proactive user responses. As a result, the number of replies consumers may have in a given situation is reduced, and design and programming effort is reduced.
  • Step 2: Once your chatbot developer can respond to inquiries in natural language, attempt detecting the user’s NL words when proactive and make the effort. API.ai, IBM Watson, Wit.ai, and other toolkits can let you add natural language processing to your chatbot certification. Understanding other input types, such as emoticons, emojis, gifs, and photographs, is also part of intelligent perception. An intelligent chatbot training must deduce the user’s intents and answer correctly based on visuals.

Learning

Learning is another intelligence attribute that your chatbot developer might have. Does your chatbot certification have the ability to learn? Is it able to learn from its mistakes and improve its performance over time? Individual chatbot modules, such as natural language comprehension and user modeling, can learn to perform better over time with the help of machine learning (ML) algorithms and human supervisors. Machine learning approaches are divided into three categories: supervised, unsupervised, and reinforcement learning. A variety of algorithms can be used to achieve each of these goals. Supervised learning can be used to classify user intents from user utterances. Unsupervised clustering methods can detect user clusters based on their conversational behaviors. Reinforcement learning algorithms can also learn efficient and effective conversation behaviors (e.g., what should the bot say next?).

There are numerous methods to express the same idea (i.e., user intent). Assume your chatbot can distinguish between N different user intents. Intentions can be stated in a variety of ways. However, a chatbot’s initial version may not feature all of these. As a result, even while it can respond to the user intent represented by the speech, it will fail to interpret the user’s utterance. Such occurrences can be recorded, labeled, and put into a machine learning module. Your chatbot training can comprehend consumers better over time by iterating over missed expressions using machine learning.

One thing to keep in mind, though, is that machine learning algorithms learn from the data and experience provided to them. As a result, the quality of such data and experience is critical. It’s a good idea to collect enough data using a handcrafted system before employing machine learning methods to improve its performance. Weka and Google’s TensorFlow are two toolkits that can execute ML tasks.

Planning

Planning is the third component of intelligent behavior. Planning is an internal process performed by the chatbot to determine how to do the task asked by the user. Simple tasks, such as user surveys do not necessitate much planning. The bot must proceed from one question until the task is completed. However, if the bot is expected to perform strenuous activity, it must determine the sequence of actions that will lead to the user’s goal. A plan is a set of actions carried out in a specific order. Conversational behaviors such as inquiring, informing, acknowledging, and so on would be included in these plans.

Currently, developers break down jobs themselves and provide ready-made plans to chatbot developer. However, if a chatbot is to multitask, it must devise its strategies. This will eliminate the need for chatbot designers and developers to map every step of a possible conversational flow between users and chatbots.

To put this in context, a chatbot with planning capabilities will devise a series of activities to accomplish a user’s goal. If a customer inquires about delayed delivery status, the chatbot (working for a retailer) will determine which database or API to access. It will then determine if it has all of the essential parameters to make such a query once it has figured out where the information is.

Conclusion

Chatbots can be made more intelligent than now by using natural language understanding, machine learning, and AI planning algorithms. This isn’t to argue that all chatbots should be intelligent. However, as chatbots face more users and more user-centric activities, developing them more innovative to handle both jobs and natural dialogues with users will become a necessity.

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