Language Generation for Conversational Agent by Evolution of Plan Trees with Genetic Programming

Abstract

Abstract. As dialogue systems are widely demanded, the research on natural language generation in dialogue has raised interest. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains answers to support more flexible and customized dialogues with humans. The paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the sentence structure, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is obtained. The proposed method applies to a dialogue-based travel planning system and the usability test demonstrates the usefulness of the proposed method.

A Review of the Practical Applications of Pedagogic Conversational Agents to Be Used in School and University Classrooms

“…Hence, the fifth criterion would be: The sixth criterion is the adaptative-evolution possibilities. It has been included from the inspiration of reading the work of Liebermen [55], in which he claimed that autonomous agents can show adaptive and evolution possibilities, i.e., the agent adapts its behavior to each student, and learns how to evolve to interact better depending on previous interactions [56][57][58][59]. PCAs may not have either adaptive or evolution possibilities as in 6.1, to have adaptive possibilities to modify their dialogue and/or gestures to the student as in 6.2, to have evolution possibilities and to learn from the previous sentences of the dialogue to generate different interactions (verbal and or non-verbal) as in 6.3, and even to have adaptive and evolution possibilities so that from the learning of previous and present interactions the behavior and/or dialogue of the agent changes as in 6.4.…”

“…Agents and advances in Natural Language Processing could improve the interaction with the students showing more complex animation, so that agents become able to adapt their conversation and behavior to the conversation and behavior to the students [57,58,61].…”

Overview of Interactive Genetic Programming Approaches for Conversational Agents

“…EAs can provide solutions to some of those problems. For instance, EAs can generate: text structures for discourse planning (Karamanis & Manurung, 2002); referring expressions (Hervás & Gervás, 2005); and, dialogues (Kim et al 2004;Lim & Cho, 2005).…”

“…This approach was taken by Lim & Cho (2005) with the same goal than Kim et al (2004): improving the response adaptability in conversational agents by responding with sentences constructed through an evolutionary process. The fitness is also evaluated according to how natural the user thinks that the queries are.…”

“…-The difficulty in designing a correct grammar which covers all possibilities. Lim & Cho (2005) also claimed that the use of grammar-based approaches in Interactive Genetic Programming for conversational agents has the defect of making wrong sentences if the algorithm does not have enough time for evolution.…”