Changes for page Deliberation based on reflection processes (NLP (NLU/NLG))

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5	5	An overview of chatbots has seen the growth of conversational agents from pattern matching agents and rule-based agents to generation-based conversational agents and reinforcement learning approaches used in the creation ChatGPT. To allow for the best interaction with the system, we want the system to be able to learn from the users' inputs, so in this case we will aim to recognise contextual clues about texts from humans and responds based on those clues. We want our systems thus to understand when users are using words to reflect a certain value, to reflect seamless interaction between the agents.
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7		-perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions—anticipated or not—with relevant and helpful answers in their own words.
	7	+The system's interaction is divided into 4 parts:
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9		-The NLTK includes libraries for many of the NLP tasks listed above, plus libraries for subtasks, such as sentence parsing, word segmentation, stemming and lemmatization (methods of trimming words down to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the computer better understand the text). It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.
	9	+~1. The user generates text and inputs it into the conversational agent.
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11		-"We have presented five studies which show that experienced computer users do in fact apply social rules to their interaction with computers, even though they report that such attributions are inappropriate. These social responses are not a function of deficiency, or of sociological or psychological dysfunction, but rather are natural responses to social situations. Furthermore, these social responses are easy to generate, commonplace, and incumble."
	11	+2. The input is analysed by the conversational agent to decipher the meaning of the input. The system wants to decipher the system's intent. It does so utilising natural language understanding (NLU).
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	13	+3. The system is attempting to manage the dialogue. In doing so, the system is generating dialogue by formulating a response that mimics human language. It does so by using natural language generation (NLG).
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	15	+4. The system uses reinforcement learning to refine its responses over time based on how well the conversational agent did in each iteration.
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14	14	**References:**
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16	16	M. Mori, K. F. MacDorman and N. Kageki, "The Uncanny Valley [From the Field]," in IEEE Robotics & Automation Magazine, vol. 19, no. 2, pp. 98-100, June 2012, doi: 10.1109/MRA.2012.2192811.