MALLET - A Multi-Agent Logic Language for Encoding Teamwork

Xiaocong Fan, John Yen, Michael Miller, Thomas R. Ioerger, Richard Volz

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

MALLET, a Multi-Agent Logic Language for Encoding Teamwork, is intended to enable expression of teamwork emulating human teamwork, allowing experimentation with different levels and forms of inferred team intelligence. A consequence of this goal is that the actual teamwork behavior is determined by the level of intelligence built into the underlying system as well as the semantics of the language. In this paper, we give the design objectives, the syntax, and an operational semantics for MALLET in terms of a transition system. We show how the semantics can be used to reason about the behaviors of team-based agents. The semantics can also be used to guide the implementation of various MALLET interpreters emulating different forms of team intelligence, as well as formally study the properties of team-based agents specified in MALLET. We have explored various forms of proactive information exchange behavior embodied in human teamwork using the CAST system, which implements a built-in MALLET interpreter.

Original languageEnglish (US)
Pages (from-to)123-138
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2006

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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