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A literature review of the implementation of Dialogue based Natural Language Chatbot and their practical applications Lok Hey Young and Wang Xiang Advisor: Dr. Supratip Ghose 1 Abstract. In this paper, a survey was conducted on current Recommender Systems with Natural Language based Dialogue systems like Chatbot. In particular, we investigate the potential of Alice Chatbot systems to work as Recommender System or perform as a Web guide in a topic-specific domain. The conclusion of the survey was that the proper way of knowledge acquisition in Natural language Chatbot can contribute a significant improvement to recommender systems and can act as a webguide to any suitable domain. Keywords: Natural Language Chatbot, Dialogue Systems, Alice Chatbot, Recommender Systems, Web Guide, Knowledge Acquisition 1 Introduction It is a great trade-off for the information revolution that end-users are finding it increasing challenging to locate relevant information and services quickly and easily. The traditional Web search engine, for example, is an essential tool for information access, but even today’s leading search engines are finding it difficult to cope with the continued growth of the Internet and the poor quality of typical user queries. While phenomenally successful in terms of amount of accessible content and number of users, today’s web is a relatively simple artifact. Web content consists mainly of distributed hypertext and hypermedia accessible via keyword-based search and link navigation. Simplicity is one of the Web’s great strengths and an important factor in its popularity and growth; even naïve users quickly learn to use it and even create their own content. However, the explosion in both the range and quality of Web content also highlights serious shortcomings in the hypertext paradigm. The required content becomes increasingly difficult to locate via search and browse; for example, finding information about people with common names can be frustrating. Many electronic-commerce sites attempt to solve the problem by providing keyword search capabilities. However, keyword search engines usually require that users know domain-specific jargon so that the keywords could possibly match indexing terms used in the product catalogue or documents. Keywords search doesn’t allow uses to precisely describe their intentions or specify relational operators (for example, less than, cheapest) on product attributes. A search for shirt can reveal dozens or even hundreds of items, which are useless for somebody who has a specific style and pattern in mind. Moreover, keyword search systems lack an understanding of the semantic meaning of the search words and phrases. For example, keyword search systems usually cannot understand that summer dress should look up in women’s clothing under dress, whereas dress shirt is most likely in men’s under shirt. Finally, search engines do not accommodate business rules, for example, a prohibition against displaying cheap earrings, with more expensive ones. Given the context, Personalization became an important business strategy in Business to Consumer commerce, where a user explicitly wants the e-commerce site to consider her own information such as preferences in order to improve access to relevant products. Recommender systems have become integral to e-commerce, providing technology that suggests products to a visitor based on previous purchases or rating history. Recommender Systems offer a solution to the problem of successful information search in the knowledge reservoirs of the Internet by providing individual recommendations. A chatterbot (chatbot) system is a software program that interacts with users using natural language. It is capable of engaging in conversation with a user. In most applications chatterbots are used as guides who can show the user around on a website. The purpose of a chatbot system is to simulate a human conversation; the chatbot architecture integrates a language model and computational algorithms to emulate informal chat communication between a human user and computer using natural language. Given the current trends for personalization and Recommender systems, user preferences can be extracted from the dialogue session with ALICE and from this user profile can be built to recommend users relevant products or to navigate them successfully in a big websites with thousand products and pages in which user can be easily lost in the vast amount of information. Therefore, the survey takes the objective to study the implementation of Chabots and finds its different application the other types of dialogue systems in order to complement the search system. This survey investigates the knowledge acquisition activities of a chatterbot program that mimic human conversation. Moreover, this paper investigates how the chatterbot program as a guide and how can it be used as a tool for Knowledge acquisition. Besides, it shows how it is useful in daily life, such as help desk tools, automatic telephone answering systems, tools, to aid in education, business and ecommerce. Web-based chatterbot system can provide an easy, natural extension to knowledge acquisition and recommender systems. An artificially intelligent natural language chat robot would be of great value in several situations. For example, consider a university environment. Even though most of the information is available on the web, students often like to have personal interaction with the advisor. In such an environment, a chat robot could be designed for providing academic advice. In business, chat robot can answer questions on various government services or provide social security information. A chat robot serving in above environments will have to understand natural language inputs from users, locate the requested information, and disseminate information through a natural language interface. The survey summarizes the existing literature on ALICE chatbot and how they fit into the framework of current information systems paradigm. In section 2, the study reflects on algorithm and general structures of ALICE Chatbot and its application with information Systems. In section 3, the study summarizes the main techniques taken from the literatures and in section 4 the study will draw the conclusion regarding the survey. 2 Literature review The main topic of this survey was to study on Alice Chatbot, its algorithm and language. The survey intends to extend the algorithm of Alice Chatbot so that it can be served as web guide. Therefore, in the subsequent sections the study focuses on points on the construction of ALICE and how ALICE is used in different systems and application areas. 2.1 Alice Chatbot A.L.I.C.E. is an artificial intelligence natural language chat robot based on an experiment specified by Alan M. Turing in 1950. The A.L.I.C.E. software utilizes AIML, an XML language we designed for creating stimulus-response chat robots. Some view A.L.I.C.E. and AIML as a simple extension of the old ELIZA psychiatrist program [Weizenbaum, J. 1966]. The comparison is fair regarding the stimulus-response architecture. But the A.L.I.C.E. bot has at present more than 40,000 categories of knowledge, whereas the original ELIZA had only about 200. Another innovation was provided by the web, which enabled natural language sample data collection possible on an unprecedented scale. A.L.I.C.E. won the Loebner Prize, an annual Turing Test, in 2000 and 2001. Although no computer has ever ranked higher than the humans in the contest she was ranked “most human computer” by the two panels of judges. What it means to “Pass the Turing Test” is not so obvious. Factors such as the age, intellect and expectations of the judges have tremendous impact on their perceptions of intelligence. Alan Turing himself did not describe only one “Turing Test.” His original imitation game involved determining the gender of the players, not their relative humanness. ALICE uses XML knowledge bases to match user input against a predefined response set. The shortcoming of this system is that it cannot adequately answer all of the queries given to it as Russesl et al. in [2] contends that ALICEBOTs have no cognitive theory behind them, instead they blindly rely on canned response to matched queries. Alicebots are also able to expand their present knowledge bases through XML based AIML (Artificial Intelligence Markup Language) (Wallace 2003). This would imply that ALICEbots could be given an ‘expert appearance’ within a particular domain of knowledge. This expansion has already been witnessed in the areas of foreign language fluency, and specific domain-related knowledge fields which can either be supervised by interceding chatterbot master or unsupervised where knowledge is gathered en masse from trusted sources. In this section, the main features of ALICE are briefly presented and more information can be found at A.L.I.C.E: Artificial Intelligence Foundation. A.L.I.C.E matches the user input with a set of stored categories in reverse lexicographical order. The categories are specified in Artificial Intelligence Markup Language (AIML). Each category comprises of one or more patterns and a template. If any of the patterns match with the user input, the string specified in the template would the robot’s response. For example, consider the following category: <category> <pattern>HI</pattern> <template> Hello, nice </template> </category> to meet you. When the user input matches the patter “HI”, A.L.I.C.E. responds with the template reply “Hello, nice to meet you. “ Even though the above strategy appears to be a naïve pattern matching approach, when used n conjunction with other techniques it can generate seemingly intelligent responses [Wallace, R.S. 2000]. A.L.I.C.E. provides a powerful capability named symbolic reduction [Wallace, R.S. 2000]. The symbolic reduction can be used to “jump” from one category to another. For example, consider the case where the user input “Hello, how are you” is matched with the pattern specified in one of the categories. Another user input, “Hi, how are you?” could be matched with a different category. Even though the matching categories in both cases might be different, the two user inputs essentially mean the same thing. So, instead of specifying individual responses for the two different inputs, they all could be mapped to single category (“how are you”) by using symbolic reduction. There are three types of categories: atomic categories, default categories, and recursive categories. a. Atomic categories: are those with patterns that do not have wildcard symbols, _ and *, e.g.: <category> <pattern>10 Dollars</ pattern> <template>Wow, that is cheap. </ template> In the above category, if the user inputs ’10 dollars’, then ALICE answers ‘WOW, that is cheap.’ b. Default categories: are those with patterns having wildcard symbols * or _. The wildcard symbols match any input but they differ in their alphabetical order. Assuming the previous input 10 Dollars, if the robot does not find the previous category with an atomic pattern, then it will try to find a category with a default pattern, such as: <category> <pattern>10 *</pattern> <template>It is ten.</template> </category> So ALICE answers ‘It is ten’. c. Recursive categories: are those with templates having <srai> and <sr> tags, which refer to recursive reduction rules. Recursive categories have many applications: symbolic reduction that reduces complex grammatical forms to simpler ones; divide and conquer that splits an input into two or more subparts, and combines the responses to each; and dealing with synonyms by mapping different ways of saying the same thing to the same reply. c.1 Symbolic reduction <category> <pattern> DO YOU KNOW WHAT THE * IS </pattern> <template> <srai> What is <star/× /srai> <template> </category> < In this example <srai> is used to reduce the input to simpler form “what is *”. c.2 Divide and conquer <category> <pattern> YES *</pattern> <template> <srai> YES</srai> <SR/> <template> </category> The input is partitioned into two parts, “yes” and the second part; * is matched with the <sr/>tag. <sr/>=<srai>/></srai> c.3 Synonyms <category> <pattern> HALO</pattern> <template> <srai> Hello< /srai> <template> </category> The input is mapped to another form, which has the same meaning. ALICE Pattern matching algorithm Before the matching process starts, a normalization process is applied for each input, to remove all punctuation; the input is split into two or more sentences if appropriate; and converts the input to and uppercase. For example, if input is: “I do not know. Do you, or will you, have a robots.txt file?” Then after the normalization it will be: “DO YOU OR WILL YOU HAVE A ROBOTS DOT TXT FILE”. After the normalization, the AIML interpreter tries to match word by word to obtain the longest pattern match, as we expect this normally to be the best one. This behaviour can be described in terms of the Graphmaster set of files and directories, which has a set of nodes called “nodemappers” and branches representing the first words of all patterns and wildcard symbols (Wallace, 2003). Assume the user input starts with word X and the root of this tree structure is a folder of the file system that contains all patterns and templates, the pattern matching algorithm uses depth first search techniques: 1. If the folder has a subfolder starts with underscore then turn to , “_/”, scan through it to match all words suffixed X, if no match then: 2. Go back to folder, try to find a subfolder start with word X, if so turn to “X/”, scan for matching the tail of X. Patterns are matched. If no match then: 3. Go back to the folder, try to find a subfolder start with star notation, if so, turn to “*/”, try all remaining suffixes of input following “X” to see if one match. If no match was found, change directory back to the parent of this folder, and put “X” back on the head of the input. When a match is found, the process stops, and the template that belongs to that category is processed by the interpreter to construct the output. There are more than 50,000 categories in the current public-domain ALICE “brain”, slowly built up over several years by the Botmaster, Richard Wallace, the researcher who maintained and edited the database of the original ALICE. However all these categories are manually “hand –coded”, which is time-consuming, and restricts adaptation to new discoursedomains and new languages. In the following section we will present the automation process we developed, to re-train ALICE using a corpus based approach. 2.2 Chatbots and Dialogue System A number of chat robots have been developed to serve different application domains. Their design has become increasingly sophisticated and their use adopted in education (Jia J.), commerce [Angeli D., Creative Virtual], entertainment [Wacky Web], and the public sector [e.g., West Ham and Plainstow NDC]. ELIZA was developed to serve as a psychoanalyst who simply frames questions deriving from the user input itself. ELIZA, though an innovative program, is “memory less” and does not really “understand” user inputs. EGAIN CHATBOT and IKEA [IEKA.com] help centre chatbot is a lifelike conversational agent providing an interactive and personal way for users to get answers and assistance on any website. A customer simply chats with an assistant, and the assistant acts as an agent, providing answers, processing data and solving customer problems. A chatbot provides frontline support so the customer service staff can concentrate on more complex tasks. A chatbot on the business sites are regarded as shopping bots and upon installation will Greets uses on the site, answer FAQ, take uses on a tour of a site and conduct surveys. It learns from each customer visit and keeps them coming back by remembering the sequential visits of the customers and can prompt users based on prior conversations. Those ChatBots have a reporting tool that allows vendors to follow his customers thought process and identify ways to enhance the site to better meet their needs. Reporting provides real-time access to all customer dialogs, and a query tool for generating graphical reports showing how one customer is responding to survey questions. Moreover, since your assistant always asks permission to gather information, customer’s privacy is assured. Commercial Chatbot can be engaging and those have personality and character of a human representative- one that provides entertainment and support, and evokes trust, commitment, and loyalty in users. In studies conducted on ALICEbots in particular, some interesting results have been obtained. One study focused on using an ALICEbot as a Social Theory tutor for students. It was discovered that students were more interested in using the system as a search engine to answer assignment questions rather than as the conversational tutor per its design. This sentence-based information retrieval aspect has traditionally been confirmed to the arena of search engine design. In another learning-style experiment in [Jia 2002], a modified version of an ALICEbot was used as a learning tool to teach Chinese students either English or German. The study focused more on user attitudes rather than on chatterbot efficieny. It was discovered that 62% of users chatted for 10 lines or less, and that 8.5% of the time ALICEbot has no specific pattern to match the given input and had to rely on root-level genric resposes. These conversational entities all have in common the difficulty of maintaining dialogue for sustainable period of time. YPA is a natural language Chatbot dialogue systems that allows user to retrieve informaiton British Telecom’s Yellow pages [Kruschwithz et. 1999, 2000]YPA is composed of Dialogue manager, Natural Language Front-End, Query Construction Component, and the backend database. Dialogue systems carry out conversations with users in natural language, whether spoken or typed. The main tasks performed by dialogue systems are language interpretation, language generation, and dialogue management. The simplest dialogue managers are based on finite-state automata in which the states correspond to questions and arcs correspond to actions that depend on a user-provided response [Stent, Dowding 1999; Winograd Flores, 1986]. These systems support what are called fixed- or system-initiative conversations, in which only one of the participants controls the actions, whether it is the system helping the user or the user asking question of the system. Next, in complexity are frames or template based systems in which questions can be asked and answered in any order [Bobrow et al., 1977]. Next, true mixed-initiative systems allow either dialogue participant to contribute to the interaction as their knowledge permits [Allen, 1999; Haller & McRoy, 1998; Pieraccini, Levin, and & Eckert, 1997]. Thus, the conversational focus can change at any time due to the user’s (or system’s) initiative of that change. Finally, some different approaches that support sophisticated dialogues include plan-based systems [Allen et al., 1995; Cohel & Perrault, 1979] and systems using models of rational interaction [Sadek, Bretier, & Panaget, 1997]. 2.3 Recommender Systems and Natural Language Assistant Natural Language Assistant [NLA] allows customers to make requests in natural language and directs them towards appropriate web pages. For example [Joyce C., 2002] presented an architecture in which the NLA was deployed in a pilot study at an IBM external web site in order to drive Natural Language Search. The work involves with the system is to engineer dialog for presenting the merchandise to the user, using user interface studies to guide both the form and the content, and designing the system to support business rules and business processes for updating the data, for example, when offerings change. A natural language search with the user specification can be formulated “The fastest Computer under $1400”. Based on the understanding of the input, NLA retrieves the laptop (a Thinkpad R30 model) that has the fastest central processing unit (CPU) speed among all laptops with a price less than $1400. This example demonstrates the tremendous advantage of natural language search because the user is able to obtain the desired product in one interaction rather than navigate up and down several layers of menus (menu-driven navigation) or browse among several irrelevant pages (Keyword search). The results showed that the users clearly preferred dialog-based searches to non-dialog based searches. Asking users question about their lifestyle and how they were going to use computer accounted for improved performance for directed dialog systems. The Adative Place Advisor suggested by [Cynthia A. T., Goker H. M. and Pat L] presented a novel user model that influences both item search and the questions asked during a conversation. This is one of the fist conversation based spoken dialog systems and build a model of user preference from conversations. 2.3 Knowledge Acquisition Techniques with ALICE The process of knowledge acquisition is to transfer existing knowledge and its structure into a computer-interpretable form (Potter 2001). When coupled with the internet, knowledge acquisition inherits new problems of scale such as information quality and reliability issues. Knowledge acquisition has been a sought after goal since the early days of Artificial Intelligence. The automated or semi-automated approaches have several sub-approaches; pattern and template matching, and text data mining (Lavrac and Mozetic 1992; potter 2001). In pattern and machine learning, natural language input I matched against some preconceived template where the response is pre-coded and dictated by the matching template. One of the approaches is Eliza-type chatter bot, which played the role of a Rogerian psychotherapist to pseudo extract information from its patients (Weizenbaum 1966). With Eliza, it became possible to carry on generalized conversations in a reasonable manner. Dialog system can adequately carry out the conversations with the user and can log the conversations which can be good source for knowledge acquisition for domain specific topic. Therefore, techniques of knowledge acquisition were rightly used in [Schumaker P. R., Liu Y., Ginsburg M., Chen H] with their system AZ-ALICE chatbot that is a extension in ALICE chatbot. They tested their system in a domain specific knowledge acquisition as testbed with a number of participants and it was shown that mass knowledge acquisition improved the domain-specific chatterbot responses. They hypothesized that use of a chatterbot as a knowledge acquisition tool appears to be a stable instrument in gathering both conversation and domain-related knowledge and showed with statistics that it would show a promising future in domain restricted areas. 3 Implementation of the Project and Hypothesis The implementation of this project in a university environment is indeed something very new. This Help-bot can be used to assist the Graduate Advisor in giving advice to students. It is hoped that this help-bot will lessen the current workload of the Graduate Advisor. In this project, the botmaster was the authors of the survey and A Botmaster is the master of the chatbot. Our responsibilities included reading the dialogues, analyzing the responses, and creating new replies for the patterns. We intend the serve the UIC graduate advisor with our Chatbot and implement it as a help-bot that complements the Academic Registry of UIC to advise undergraduate and graduate student. The main from the surveys is the following: 1. Can we store the correct student model so that the chatbot can successfully recommend or advise a graduate with his/her coursework? 2. Can the conversation be driven in particular manner so that it can successfully elicit the knowledge from the conversation log and increased the responses from the user. 3. Can it help a Navigation system as large as UIC to find the information properly? 4 Conclusion of the Survey Chat bots are becoming very common in many web sites today. They serve to give personality to web sites. It is deemed from the survey that Chatbot can be used as a help-bot to successfully guide a user in a vast navigation space. Next, it can be used successfully in many systems as conversation recommendation. The use of chatbot can be used to gather domain specific knowledge and can be correctly used as a FAQ system in a Graduate advisor system. References A.L.I.C.E.: Artificial Intelligence Foundation: ALICE and AIML Documentation, http://www.alicebot.org/documentation Allen, J.F. (1995). Natural Language Understanding (second edition). Benjamin/Cummings, Menlo Park, CA. Chai, J., Horvath V., Nicolov, N., Stys, N., K., Zadrozny, W., Melville, P. (2002).Natural Lanuage Assistant: A dialogue system for online product recommendation. AI Magazine; Summer 2002; 23, 2, pp 63-75 ProQuest Science Jounals. Creative Virtual. UK Lingubot Customers. 2004-2006. Listing of major companies using Linubot technology. www.creativevirtual.com/customers.php. Accessed 14/12/05 Cohen, W., Schapire, R., & Singer, Y. (1999). Learning to order things. Journal of Artificial Intelligence Research. 10, 243-270. 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Wacky Web Fun Ltd. RacingFrogz.org c 2005. Online game with chatbot capabilities. www.racingfrogz.org. Accessed 17.05. 06 Wallace, R.S. 2000. Symbolic Reductions in AIML, http://alicebot.org/srai.html Weizenbaum, J. 1966. ELIZA- A Computer Program for the Study of Natural Language Communication between Man and Machine, CACM 9(7), 36-43