Full metadata
Title
An investigation of topics in model-lite planning and multi-agent planning
Description
Automated planning addresses the problem of generating a sequence of actions that enable a set of agents to achieve their goals.This work investigates two important topics from the field of automated planning, namely model-lite planning and multi-agent planning. For model-lite planning, I focus on a prominent model named Annotated PDDL and it's related application of robust planning. For this model, I try to identify a method of leveraging additional domain information (available in the form of successful plan traces). I use this information to refine the set of possible domains to generate more robust plans (as compared to the original planner) for any given problem. This method also provides us a way of overcoming one of the major drawbacks of the original approach, namely the need for a domain writer to explicitly identify the annotations.
For the second topic, the central question I ask is ``{\em under what conditions are multiple agents actually needed to solve a given planning problem?}''. To answer this question, the multi-agent planning (MAP) problem is classified into several sub-classes and I identify the conditions in each of these sub-classes that can lead to required cooperation (RC). I also identify certain sub-classes of multi-agent planning problems (named DVC-RC problems), where the problems can be simplified using a single virtual agent. This insight is later used to propose a new planner designed to solve problems from these subclasses. Evaluation of this new planner on all the current multi-agent planning benchmarks reveals that most current multi-agent planning benchmarks only belong to a small subset of possible classes of multi-agent planning problems.
For the second topic, the central question I ask is ``{\em under what conditions are multiple agents actually needed to solve a given planning problem?}''. To answer this question, the multi-agent planning (MAP) problem is classified into several sub-classes and I identify the conditions in each of these sub-classes that can lead to required cooperation (RC). I also identify certain sub-classes of multi-agent planning problems (named DVC-RC problems), where the problems can be simplified using a single virtual agent. This insight is later used to propose a new planner designed to solve problems from these subclasses. Evaluation of this new planner on all the current multi-agent planning benchmarks reveals that most current multi-agent planning benchmarks only belong to a small subset of possible classes of multi-agent planning problems.
Date Created
2016
Contributors
- Sreedharan, Sarath (Author)
- Kambhampati, Subbarao (Thesis advisor)
- Zhang, Yu (Thesis advisor)
- Ben Amor, Heni (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 73 pages : color illustrations
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.40330
Statement of Responsibility
by Sarath Sreedharan
Description Source
Viewed on December 5, 2016
Level of coding
full
Note
thesis
Partial requirement for: M.S., Arizona State University, 2016
bibliography
Includes bibliographical references (pages 71-72)
Field of study: Computer science
System Created
- 2016-10-12 02:21:31
System Modified
- 2021-08-30 01:21:08
- 3 years 2 months ago
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