Rule-Based Conversation -Draft

This entry is part 1 of 13 in the series The State of Chatbots

The majority of current chatbots use a rule-based approach to interact with the user. In order to imitate human conversations, developers use a linguistic model combined with computational algorithms to build chatbots. Depending on the chatbot, rules can be based on simple textual pattern matching or complicated rules based on inference mechanisms (De Angeli & [...]

Request for Comments

This entry is part 1 of 13 in the series The State of Chatbots

I am requesting for comments on my research, please email me rwhetsel AT ravensong DOT com, for user rights if you are interested in helping me. FYI: I will be posting my references and bibTex files for my literature review when I finish my thesis. Chatbot Literature Review: The State of Chatbots “Do Chatbots Dream [...]

Abstract -Draft

This entry is part 2 of 13 in the series The State of Chatbots

As humans we like familiarity, so it makes us more comfortable when we are able to interact with our computer systems using natural language. Chatbots are applications that have the ability to converse with users using natural language. However, while increasing numbers are being developed for public use, the challenge remains to make chatbots better [...]

How do Humans View Chatbots? -Draft

This entry is part 2 of 13 in the series The State of Chatbots

Since chatbots have moved into the public realm, there has been interest in evaluating how users interact with chatbots. For example, after analyzing conversations with the chatbot jabberwacky, one study found that the topics and style of conversations were broad (De Angeli & Brahnam, 2008). Users displayed different attitudes ranging from nice to nasty and [...]

Introduction -Draft

This entry is part 3 of 13 in the series The State of Chatbots

Philip K. Dick wrote a science fiction novel in 1968 entitled, “Do Androids Dream of Electric Sheep?” where he explored the philosophical issue of what it is to be human (Dick, 1968). This begs the question of how to define intelligence as it relates to a machine. In 1950, Alan Turing proposed a methodology to [...]

Building Better Chatbots -Draft

This entry is part 3 of 13 in the series The State of Chatbots

The major goal of building a better chatbot is to improve its interactions with humans. A wide variety of techniques including developing different architectures, incorporating quirks or tricks, or using different machine learning approaches have been used to improve either chatbot accuracy or human-like qualities. First, some ways that researchers are trying to improve human-like [...]

Outline -Draft

This entry is part 4 of 13 in the series The State of Chatbots

This paper will focus on chatbots as natural language interfaces. Significant advances in chatbot development from the 1960s to the present will be discussed, followed by practical application of chatbots in public arenas. Details on the way that chatbots handle user-inputted text and respond will be presented. The paper will then discuss user views of [...]

Improving Human-Like Qualities of Chatbots -Draft

This entry is part 4 of 13 in the series The State of Chatbots

Some researchers are designing chatbots with the intent of integrating more natural human-like interactions (De Angeli & Brahnam, 2008). Specifically, the developer may program certain responses that would sacrifice accuracy, but confer more human-like traits (Sing et al., 2006). For example, if a chatbot is presented with a math problem it could wait, as if [...]

History – Significant Advances in Chatbot Development -Draft

This entry is part 5 of 13 in the series The State of Chatbots

ELIZA is considered one of the rst chatbots and was created at MIT by Weizenbaum (Weizenbaum, 1966). She was designed to be a psychotherapist, basing rules on the way psychotherapists ask  questions. Weizenbaum used his LISP list-processing language so that ELIZA could simply parse and substitute key words into canned phrases. The chatbot was rule-based [...]