Exploring Chatterbots: From ELIZA to Claude and Beyond

1. WHAT IS A CHATTERBOT? A chatterbot is a computer program that simulates a conversation between two people. That is, one person writes something and the chatterbot responds, or the chatterbot writes something and the chatterbot responds to its own input. Some popular examples of chatterbots include the HAL 9000 from 2001: A Space Odyssey, David from Steven Spielberg’s Artificial Intelligence, and the infamous Johnny Five from Short Circuit. Notwithstanding, these are robots with integrated chatterbot technology. But I those examples give you a good idea about what a chatterbot does. ** KEY POINTS **

2. ABOUT ELIZA The first chatterbot created was by Professor Joseph Weizenbaum, a MIT professor, in 1976. It was named ELIZA. It’s purpose was to simulate a conversations between a psychoanalyst and a patient. HOW ELIZA WORKS Eliza’s algorithm works repeating what you wrote in question form. It’s quite primitive, and that leads to many problems in conversations. Hi, I’m Eliza. What do you want to talk about? Hamburgers What does that suggest to you? That I want to talk about nonsense Why do you want to talk about nonsense? The pink elephant made me do it. The pink elephant made you do it? Indeed. ** RESEARCH **

3. ABOUT CLAUDE Claude is the work of Brian McLaughlin, and is a successor to Racter. Its purpose is unknown to me. HOW CLAUDE WORKS Unlike Eliza, Claude recognizes keywords you enter and develops a response based on them using responses in his database. However, it does not respond when it cannot find a response in its database. Are you implying we’re flirting? Maybe I am. So what else is new? Every niece stands the world on its ear sometime. How true. My neighbor picks his nose then flicks it at his little brother. So you want to talk about neighbors? Well, I’ve just about had my fill of neighbors. I think neighbors are merely loud, don’t you? My neighbors are quiet. I think they may eat their young. ** RESEARCH **

4. RESEARCH CONCLUSIONS Although you have only seen two examples of chatterbots, the reasons those two were picked was that both sufficiently summarize the way all chatterbots are implemented. A chatterbot performs a keyword match on its input. If nothing is found that matches a particular string in its database, it repeats the input in the form a question. If the keyword is found, then it selects a response at random. Also, although you can’t see it, the aforementioned chatterbots delay their output to simulate human typing. But that’s not fooling anyone. Confused? It will all be explained shortly. ** CONCLUSIONS **

5. KNOWLEDGE BASE VS. THE STATIC DATABASE Most of what the chatterbots responds with is found in its dictionary file, or its knowledge base. You’ll see an example of this shortly. Responses to when the user repeats himself, enters a greeting, or when the chatterbot does not understand what the user is saying is when the chatterbot uses responses from its static database. That is, it copies the input to a cache, searches for words to transpose, and repeats the input as a question. ‘TRANSPOSE’ DEFINITION Just incase you didn’t know… to transpose a word is to change one word into a different context. I to you, Me to You, Am to Are, Were to Was, etc. ** DATABASE FILE **

6. What follows from this point on is an explanation of the chatterbot I created. I call it Mark. ** MY CHATTERBOT **

7. A FEW THINGS YOU SHOULD KNOW ABOUT MY PROJECT I am creating a chatterbot written in C which I have named Mark. This chatterbot is a CIS203 Artificial Intelligence chatterbot. That is, it’s database is comprised of answers to basic questions about the class. Ask it about a certain lesson, it replies with a brief summary and a link to the website where the notes can be found. It also has other standard responses. I did this so anyone who may want to run this as a CGI script on the course website, can easily do so. ** MY CHATTERBOT **

8. The 5 step algorithm I used for responses. • Check for null input • Check if reply warrants a response from the static database. • If No.2 is false then check for keywords in the knowledge base. • If a keyword is found, choose a response from the knowledge base at random. • Do necessary transpositions and output response. ** THE ALGORITHM **

9. i apples don’t like Transposed to ‘you’ Keyword found in database PARSING OF SENTENCE… ** THE MODEL ** So the output looks like You think they’re good with a caramel coating? Transposed word saved in cache then put in response Random response from database file

10. SAMPLE DATABASE FILE @NXT@ APPLES YOU THINK THEY’RE GOOD WITH CARAMEL COATING? APPLES ARE VERY HEALTHY. @NXT@ HELLO HI, HOW ARE YOU. HELLO BUDDY. @YES@ ARE YOU SURE HOW CAN YOU BE SURE? YOU SEEM VERY OPTIMISTIC @NXT@ NO YOU SEEM VERY PESSIMISTIC NEVER SAY NO. ** DATABASE FILE **

11. THE WONDERFUL THINGS ABOUT CHATTERBOTS IS CHATTERBOTS ARE WONDERFUL THINGS Why are chatterbots wonderful things? Aside from the fact they are as addicting as Snood, there is also the fact that you can modify their knowledge base to whatever you want, granted you understand the structure. You can make them more or less intelligent as you please. This is the feature they call extensibility, and it’s what makes a chatterbot so great. ** MY CHATTERBOT **