AGENTS VIC Meeting

Friday, 29 May 2009 @ 9:30AM.
Coffee, tea and refreshments from 9:15.

University of Melbourne, Rm 5.07, ICT Building (Dept of CSSE), 111 Barry St.

We have two very interesting talks this meeting. The first is by Michelle Blom from the University of Melbourne, titled "An Argumentation-Based Interpreter for Golog Programs". This work has been accepted into the highly-competitive IJCAI conference, and this talk is a practice run for Michelle. The second is from Lavindra de Silva from RMIT, titled "First Principles Planning in BDI Systems". Lavindra presented this work recently at another highly competitive conference, AAMAS. We will also have a brief discussion of some interesting ideas that came out of the recent AAMAS conference in Budapest. Abstracts for both talks can be found below.

Michelle Blom

An Argumentation-Based Interpreter for Golog Programs (IJCAI)

This talk presents an argumentation-based interpreter for Golog programs. Traditional Golog interpreters are not designed to find the most preferred executions of a program from the perspective of an agent. Existing techniques developed to discover these executions are limited in terms of how the preferences of an agent can be expressed, and the variety of preference types that can be used to guide search for a solution. The presented work combines the use of argumentation to compare executions relative to a set of general comparison principles, and the theory behind best first search to reduce the cost of the search process. To the best of our knowledge this is the first work to integrate argumentation and the interpretation of Golog programs, and to use argumentation as a tool for best first search.

Lavindra de Silva

First Principles Planning in BDI Systems (AAMAS)

BDI (Belief, Desire, Intention) agent systems are very powerful, but they lack the ability to incorporate planning. There has been some previous work to incorporate planning within such systems. However, this has either focussed on producing low-level plan sequences, losing much of the domain knowledge inherent in BDI systems, or has been limited to HTN (Hierarchical Task Network) planning, which cannot find plans other than those specified by the programmer. In this work, we incorporate classical planning into a BDI agent, but in a way that respects and makes use of the procedural domain knowledge available, by producing abstract plans that can be executed using such knowledge. In doing so, we recognize an intrinsic tension between striving for abstract plans and, at the same time, ensuring that unnecessary actions, unrelated to the specific goal to be achieved, are avoided. We explore this tension, by first characterizing the set of "ideal" abstract plans that are non-redundant while maximally abstract, and then developing a more limited but feasible account in which an abstract plan is "specialized" into a new abstract plan that is non-redundant and preserves abstraction as much as possible. We describe an algorithm to compute such a plan specialization, as well as algorithms for the production of a valid high level plan, by deriving abstract planning operators from the BDI program.