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ABSTRACT

Agents programmed in BDI-inspired languages have goals to achieve and a library of plans that can be used to achieve them, typically requiring further goals to be adopted. This is most naturally represented by a structure that has been called a Goal-Plan Tree. One of the uses of such structure is in agent deliberation (in particular, deciding whether to commit to achieving a certain goal or not). This paper presents new experimental results combining various types of goal-plan tree reasoning from the literature.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE

Additional Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques

General Terms:
Experimentation

Keywords:
autonomous agents, petri nets, reasoning