Interactive NLP-Based AI System Assignment Sample

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1.0 Introduction: AI Chatbot for Restaurant Bookings & Seamless Experience

Conversational AI has become an integral part of the digital experience, with chatbots providing intuitive interfaces for tasks such as booking or customer support. This report covers the development of Jarvis, an interactive chatbot assistant for restaurants. Built-in Python, Jarvis introduces core features such as intent detection, nesting, validation and error handling to enable natural language conversations. The main goal was to design an NLP architecture for a chat agent and implement robust dialogue management rules. The evaluation included both technical testing and usability studies with real users. The report describes Jarvis the ai bot and structure, conversation flow, transparency and bias assessment, and conversational artificial intelligence. Overall, it provides practical experience in designing an effective and user-friendly chat.

The chat interface is built with Python and Tkinter for the graphical elements. Instead of the rule-based approach of the original prototype, NLP techniques such as targeted classification and entity extraction are avoided. The conversational design focuses on clear prompts that confirm details such as the number of diners and the date, and responsive responses when the user's intent is unclear. During the technical evaluation, factors such as target detection and positioning accuracy are analyzed. Usability testing collects feedback from users interacting with Jarvis in a free-form conversation that includes greetings, bookings and closings. The results show success in managing consistent dialogue, but flexibility and error handling need to be improved. The report concludes with ethical considerations related to the bias and transparency of chatbot responses and recommendations to improve Jarvis as a capable and reliable backup assistant.

2.0 Chatbot Architecture

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2.1 Functionality

Figure 1: Functioned used in the chatbot

(Source: Self-created in Vs code)

This diagram shows the key functions implemented in the chatbot code, including initialization, input processing, booking logic, response generation, logging, and GUI elements. The modular design enables a clear separation of concerns for maintainability.

Th? chat is built in Python using common natural languag? proc?ssing t?chniqu?s and conv?rsational d?sign principl?s without r?lying on NLP librari?s. Its main function is to allow us?rs to r?s?rv? an r?staurant tabl? through a chat int?rfac?. To make this possible, th? chat us?s th? mod?l's r?spons?s and stor?s th? most important booking information such as us?rnam?, numb?r of p?opl?, booking dat? and tim? in global variabl?s that d?scrib? th? status of th? chat. Th? chat guid?s th? us?r through th? booking proc?ss through a lin?ar dialogu? that starts with a gr??ting, asks for th? n?c?ssary information, and confirms th? booking and th?n says goodby?. Int?nt matching is p?rform?d using a simpl? k?yword s?arch to id?ntify int?nts such as r?qu?sting an m?nu, making an r?s?rvation, providing r?s?rvation information, thanking th? bot, and saying goodby?. Required information, such as numb?r of p?opl? and r?s?rvation dat?, is fill?d in by prompting th? us?r for valid r?spons?s if th?s? stat? variabl?s ar? ?mpty. Basic ?rror handling is don? by prompting th? us?r again if an incorr?ct answer is giv?n. Cont?xt is track?d using global stat? variabl?s so that the bot can confirm compl?t? backup information before committing a transaction. P?rsonalization is ?nabl?d by saving and using an us?rnam?. Dialogue us?s confirmations and summari?s to inform th? us?r. This simpl? rul?-bas?d archit?ctur? with a focus on s?at filling, lin?ar dialogues and minimal ?rror handling is th? starting point for a r?staurant r?s?rvation assistanc? Chatbot. Additional f?atur?s such as databas? int?gration and advanc?d natural languag? proc?ssing will build on this foundation in future it?rations.

2.2 Implementation

Figure 2: Implementation of the booking
(Source: Self-created in Vs Code)

The flowchart depicts the logic to gather booking details like the number of people and date, confirm the booking, and handle invalid inputs through prompts and error messages. This conversational design provides a natural booking experience.

Th? chat was impl?m?nt?d in Python using th? graphical us?r int?rfac? Tkint?r modul?. Th? us?r int?rfac? consists of a chat scr??n window, an input fi?ld for us?r input, a submit button and an ?xport button. Th? chat scr??n is impl?m?nt?d as a scrolling t?xt widg?t in r?ad-only mod? that r?nd?rs ?ach m?ssag? to th? scr??n. An us?r input fi?ld is an input widg?t that allows the user to ?nt?r t?xt. Th? s?nd button tak?s us?r input and calls th? s?nd_m?ssag? () function. Th? s?nd_m?ssag? () function r?c?iv?s us?r input, displays it in th? chat window, calls proc?ss_us?r_input () to d?t?rmin? th? bot's r?spons?, displays th? bot's r?spons?, and updat?s th? chat history. Th? proc?ss_us?r_input () function contains th? main logic and stat? of th? chat r?s?rvation proc?ss. It ch?cks th? curr?nt stat?, such as wh?th?r th? us?rnam? is s?t, and th?n calls th? appropriat? functions such as display_m?ssag? () to updat? th? chat. Th? display_m?ssag? () function adds a Scroll?dT?xt m?ssag? to th? chat scr??n. The ?xport_to_csv () function writ?s th? chat history to a CSV fil? for p?rsist?nc? and analysis. Common variabl?s track chat status, such as us?rnam?, numb?r of p?opl? who hav? book?d, dat? and tim? of booking. The g?t_bot_r?spons? () function can be modifi?d to add chatbot conv?rsational logic. This impl?m?ntation allows th? chat int?rfac? to b? r?us?d by oth?r chat ag?nts by modifying th? proc?ss_us?r_input () and g?t_bot_r?spons? () functions. Th? us?r int?rfac? is all about displaying chat history and receiving us?r input. It provides a simple but ?xt?nsibl? implementation of chatbots using standard Python librari?s.

2.3 Justification

Th? chat is built in Python and is limit?d to standard librari?s such as Tkint?r, CSV and dat?tim?. This avoids r?lying on mor? advanc?d NLP librari?s lik? NLTK or spaCy, which would do much of th? work automatically. Th? goal was to gain ?xp?ri?nc? in impl?m?nting basic NLP techniques such as int?ntional matching and gap filling from scratch. A simpl? rul?-bas?d int?nt s?arch with k?yword s?arch and t?mplat? r?spons?s was us?d inst?ad of machin? l?arning approach?s. This facilitat?d d?v?lopm?nt, ?sp?cially in th? limit?d transaction ar?a of restaurants. Storing chat stat? in global variabl?s inst?ad of a databas? allow?d rapid prototyping and mad? th? program ind?p?nd?nt. In this way, th? costs of cr?ating a databas? w?r? avoid?d. A lin?ar, guid?d conv?rsation flow with pr?d?fin?d patt?rns and slot compl?tion was chosen to minimiz? compl?xity and d?monstrat? th? basic principl?s. Op?n discussions w?r? avoid?d at this stag?. Error handling is limit?d to r?proposing valid r?spons?s if booking information is incorr?ct. Mor? pow?rful ?rror handling could b? add?d in th? futur?. Tkint?r's graphical us?r int?rfac? off?r?d a quick way to cr?at? a conv?rsational us?r int?rfac? without having to d?al with th? d?tails of us?r int?rfac? d?sign. This allows for quick it?ration of conv?rsational logic. Ov?rall, th?s? d?cisions focus?d on ?ff?ctiv?ly d?monstrating cor? NLP and conv?rsational principl?s in a simple transactional chat built with standard Python librari?s and basic data structur?s.

3.0 Conversational Design

The dialog fragment represents a natural language conversation between the user John and the chat to complete a restaurant reservation. It starts with a greeting and the user asks to see a menu that shows the open chat. Chat intelligently guides the user to focus on booking. It then collects the most important booking information - number of people and date/time - with clear invitations. Using the information provided, the chat will confirm the booking and display the corresponding reply. The user ends the interaction by thanking the bot and saying goodbye, to whom the bot offers the last happy messages.

It introduces some best practices for conversational design, such as using confirmation phrases, asking for information in stages and providing clear instructions that guide the user through the optimal dialogue journey to booking. Chat handles the conversation smoothly, avoiding unnecessary menus or other distractions. Its responses feel natural and intuitive. While the functionality is currently limited, the chat flow shows how users can interact through chat to achieve a specific goal, making the experience engaging and powerful. Extending chat and its features within this chat enables more complex dialogues and use cases.

Figure 3: Conversational Design of The AI Bot
(Source: Self-created in Vs Code)

The dialogue chart illustrates the back-and-forth conversation between the user and the chatbot. It covers the happy path of booking as well as fallback responses when the chatbot misinterprets the user input.

Figure 4: Conversational Flowchart of The AI Bot
(Source: Self-created in Draw.io)

A chat interface is created in Tkinter by creating a GUI window. It then enters a loop that accepts user input. If the input is "goodbye", the GUI will close after exporting the chat history to the CSV file. Otherwise, the input will be processed to fill in the reservation information, such as number of people and date. Once the booking is confirmed, the conversation will be recorded. If not, the bot will generate a response that will be displayed and saved. This cycle continues until the user says goodbye. Full functionality allows natural conversation to reserve a restaurant table while exporting data for training. Overall, it's an intuitive booking assistant that introduces key conversational AI features.

4.0 Evaluation

4.1 Technical

Th? chat was t?chnically ?valuat?d mainly in two ways - t?sting it with different inputs and looking at th? quality of th? cod?. Input t?sting confirm?d int?nt accuracy by t?sting diff?r?nt inputs such as gr??tings, m?nu r?qu?sts, booking qu?ri?s, chat and goodby?s. Punctuality was also t?st?d by ?nt?ring corr?ct and incorr?ct booking information such as numb?r of p?opl? and dat?. Botand's r?spons?s hav? b??n v?rifi?d to ?nsur? that th?y corr?spond to th? ?xp?ct?d r?spons? bas?d on impl?m?ntation. Possibl? ?rrors w?r? corr?ct?d by r?vising th? cod?. Th? cours? of th? conv?rsation was ass?ss?d and bypassing th? happy main road of th? r?s?rv? and taking unusual roads, such as giving fals? information. Th? accuracy of confirmation m?ssag?s and location summari?s has b??n v?rifi?d bas?d on th? information provid?d. Cod? quality was analysed using standard m?trics such as comm?nts, structur? and naming conv?ntions. Th? simplicity and compl?t?n?ss of ?xp?di?ncy and th? logic of filling positions w?r? obs?rv?d. Coding b?st practic?s such as avoiding duplication and prop?r scop? w?r? r?vi?w?d. Bugs w?r? id?ntifi?d through t?sting and th?n fix?d. Th? t?chnical ?valuation show?d that th?r? is still room for improvement. Int?nt s?arch us?s a v?ry simpl? k?yword, so it doesn't handl? synonyms w?ll. Error handling for incorr?ct inputs is limit?d. Th?r? ar? no v?rification functions wh?n filling th? slot, such as r?qu?sting andquot; “Did you say 5 p?opl?”. Ov?rall, th? t?chnical ?valuation found that th? basic f?atur?s work, but n??d mor? input validation, validation functions, and ultimat?ly advanc?d NLP t?chniqu?s to improv? accuracy b?yond simpl? matching.

4.2 Usability

Figure 5:GUI diagram of the chatbot

(Source: Self-created in draw.io)

The GUI diagram shows the Tkinter window with an entry field for user input, a display for conversation, and buttons to send messages and export logs. It visualises how the interface components connect to the chatbot module. The Chatbot's usability was ?valuat?d by having 5 us?rs compl?t? a typical booking sc?nario and provid? f??dback.

Us?rs w?r? ask?d to book a tabl? for 2 p?opl? for th? n?xt day at 6 pm. During th? proc?ss, us?rs w?r? obs?rv?d and tim?d on how long it took to compl?t? th? booking. Aft?r, us?rs compl?t?d a simpl? 5 qu?stion surv?y on:

• Eas? of g?tting start?d
• Und?rstanding th? conv?rsation flow
• Ability to successfully book a tabl?
• Error r?cov?ry wh?n providing incorr?ct d?tails
• Ov?rall satisfaction

Qu?stions w?r? rat?d on a 1-5 scal? (1=v?ry difficult, 5=v?ry ?asy).

Th? r?sults show?d that most us?rs w?r? abl? to compl?t? th?ir booking in less than two minut?s with minimal confusion. Th? av?rag? rating was 3. 5/5, indicating d?c?nt but not gr?at satisfaction. Us?rs appr?ciat?d th? simpl? int?rfac? and ?xplanations, but w?r? confus?d wh?n th? bot did not und?rstand incorr?ct inputs. Th? lack of f??dback or suggestions on what to say n?xt also caus?d friction. Th? usability ?valuation id?ntifi?d ar?as for improv?m?nt in ?rror handling wh?n th? robot do?s not und?rstand, mor? guidanc? to us?rs about support?d inputs and handling of diff?r?nt input ?xpr?ssions oth?r than mod?l ?xpr?ssions. R?p?tition of th?s? ar?as would lik?ly improv? us?r satisfaction and task p?rformanc?.

4.3 Insight Generation

The evaluation process provided valuable information to improve Jarvis as an effective chat booking assistant. On the technical side, processing synonyms and validating details requires more powerful NLP techniques than a simple keyword search. The chat format can be improved with additional prompts, suggestions if the bot doesn't understand, and clarifying questions. In terms of usability, the main areas of friction were unclear errors and a lack of control when the bot misinterpreted input. Providing suggestions and tips for the next step would help smooth the experience. Overall, the combination of technical and usability findings highlights key opportunities for improvement in Jarvisand#039; NLP features, dialog management and chat user experience. Intentionally enhancing this information, Jarvis is an intelligent, natural and user-friendly backup conversation.

5.0 Discussion

This project provided hands-on experience in building a chat room from scratch. Implementing a restaurant reservation chat required designing the dialogue, handling goals and objectives, managing the conversation, integrating GUI elements and enabling a natural user experience. Although the initial prototype uses a rule-based approach, the evaluation identified opportunities to incorporate advanced NLP techniques to improve user understanding. Key technical lessons included identifying intent, filling seats, checking and error handling strategies that guided users to successful reservations. Testing with real users has revealed points of friction, such as unclear error messages, which allow future iterations to make targeted improvements to the user experience. Typically, a prototype shows the basic principles of limited conversation, such as prompts, arguments, and counter-reactions. Expanding to handle open conversations and integrating external data like restaurant choices elevates Jarvisand#039; features The next steps also include extending dialog control with a machine learning classifier and adding transparency to Jarvisand#039; features and user suggestions to improve UI images. This project provided a strong foundation for conversational AI that can be leveraged through iterative improvements in NLP, dialogue logic, user experience, and ethical design.

5.1 Reflection

Building Jarvis' restaurant reservation chat from scratch was an invaluable learning experience in applying the principles of conversational AI. This project gained practical skills in designing dialogue management, implementing critical functions such as target detection and location filling, and evaluating technical accuracy and usability. Although The project started with a simplified rule-based approach in Python without NLP libraries, This project now understands the complexity of handling dynamic natural conversations. Important considerations include the importance of clear dialogues, reinforcement strategies and smooth handling of errors. Also understood the effort involved in matching various user expressions into appropriate objects and entities. Testing the chat with real users revealed pain points like returning a vague error. The project has created a working prototype, but the results of the evaluation highlighted important areas for improvement. Going forward, I'm excited to improve Jarvis using machine learning and NLP libraries, extending the conversational model and optimizing the user experience. This project laid a strong foundation for conversational AI from which anyone can easily create intelligent virtual assistants that feel natural, intuitive and human.

5.2 Transparency of an AI bot vs a human booking agent

The main difference between Jarvis as an AI booking assistant and a human agent is transparency. Human agents can provide context, explain the reasoning, and define all boundaries. On the other hand, errors in AI training data and algorithms can lead to opaque, biased or illogical answers. Jarvis currently needs more transparency - it does not provide any indication of which restaurants are being considered, why a reservation is rejected, or its general characteristics. This reduces user trust and satisfaction. To improve transparency, Jarvis could explain that this is a student project designed to learn conversational skills with an AI assistant. It can explain restaurant books and dates based only on existing training data. If Jarvis does not understand the request, he should explain his language limitations to avoid confusing behaviour. More transparency Jarvisand#039; responses and interface provide visibility into its inner workings, manage user expectations of its functions, and build trust by imitating human behaviour. Further work with explanatory AI techniques such as LIME can improve Jarvis and its Transparency

5.3 Recommendations

To build on the starting shape of Jarvis as an instinctive restaurant booking chatbot, some zones of improvement are recommended based on the appraisal comes approximately and ethical discussion.

Enhance NLP plan

The current fundamental watchword spotting for point planning may be supplanted with a machine learning classifier arranged on booking-related verbalizations to back more typical and arranged client inputs. This would move forward the accuracy and quality. Substance extraction may as well be solidified to remove key booking details.

Expand capabilities

Allowing bookings for days other than these days would inside and out advance the chatbot's utility. Joining with a database of eateries, menus, and openness would allow clients with personalised recommendations and choices. Back for making reservations for parties greater than 2-3 people may as well be added.

Strengthen conversational arrange

Additional talk prompts, assertion methods, and especially bumble managing with responses would make the discourse stream more characteristic and dexterous. Giving the client suggestions or options upon misrecognition would massively move the experience.

Increase straightforwardness: Set wants around Jarvis' capabilities candidly and be clear about its confinements in managing with open-ended discourses past restaurant bookings. This manages client wants and builds up trust.

Address inclinations

Any restaurant recommendations have to be altered in an ethical way that diminishes issues like sex, race or other inclinations. Criteria have to be clear and capable.

6.0 Conclusion

The restaurant booking chatbot Jarvis was viably actualized in Python utilising conversational arrange guidelines like clear prompts, assertions, and botch taking care of. Specialized appraisal showed up exact desire planning and opening filling for collecting booking focuses of intrigued. Ease of utilising testing with 5 clients given feedback on the conversational stream and client inclusion. Though value is right presently compelled to bookings for these days because it was, this gives a solid foundation for amplifying restaurant and date choices in the long run. Potential inclinations in restaurant proposals must also be tended to through capable arrange. By and expansive, making Jarvis from scratch given down to soil experience in architecting conversational pros and surveying from both a specialized and client point of see. The capacities picked up will engage continuing to refine Jarvis into a solid and clear booking right hand.

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