The Anatomy of A.L.I.C.E.
By Richard S. Wallace. 2009
345 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Safety Research, Philosophy
View Paper: https://www.semanticscholar.org/paper/ed326e621ecb92320d702bfb66fc8459ffb81a36
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Xiaodong He
- Harry Shum
- Yejin Choi
- Eleni Adamopoulou
- Lefteris Moussiades
- Di Li
- Fabio Massimo Zanzotto
In Safety Research
- Xiaodong He
- Harry Shum
- Elizabeth Clark
- Noah A. Smith
- Yejin Choi
- Eleni Adamopoulou
- Lefteris Moussiades
- Di Li
In Philosophy
- Xiaodong He
- Harry Shum
- Elizabeth Clark
- Noah A. Smith
- Ari Holtzman
- Yejin Choi
- Eleni Adamopoulou
- Lefteris Moussiades
Citations in the corpus
- Sounding Board: A User-Centric and Content-Driven Social Chatbot (2018, background): “Researchers in artificial intelligence (AI) have long been interested in the challenge of developing a system that can have a coherent conversation with humans: early systems include Parry (Colby et al., 1971), Eliza (Weizenbaum, 1966), and Alice (Wallace, 2009).”
- An Overview of Chatbot Technology (2020, methodology): “ALICE relies on a simple pattern-matching algorithm with the underlying intelligence based on the Artificial Intelligence Markup Language (AIML) [11], which makes it possible for developers to define the building blocks of the chatbot knowledge [10].”
- Human-in-the-loop Artificial Intelligence (2017, background): “Chatbots(1) [39, 37] have conquered our new window-on-the-world – our smartphones – and, from there, they help with everyday tasks such as managing our agenda, answering our factoid questions or being our learning companions [16, 4].”
- From Eliza to XiaoIce: challenges and opportunities with social chatbots (2018, methodology): “Inspired by the Turing test proposed in 1950 (Turing, 1950), researchers and engineers have developed multiple conversational systems for chitchat (Weizenbaum, 1966; Colby, 1975, Shieber, 1994; Wallace 2009).”
- Preference-learning based Inverse Reinforcement Learning for Dialog Control (2012, background): “, rules) [1]; however, if the number of rules increases, it is difficult to define the rules consistently.”
- Measuring the Feasibility of a Question and Answering System for the Sarawak Gazette Using Chatbot Technology (2025, unknown): “ALICE used a simple pattern-matching algorithm with its whole intelligence specified via the Artificial Intelligence Markup Language (AIML), an XML-based markup language which allows developers to alter the facts that are known to ALICE (Wallace, 2009; Marietto, 2013).”