Use this URL to cite or link to this record in EThOS:
Title: Monitoring plan execution in partially observable stochastic worlds
Author: Wang, Minlue
ISNI:       0000 0004 5350 8457
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2014
Availability of Full Text:
Access from EThOS:
Access from Institution:
This thesis presents two novel algorithms for monitoring plan execution in stochastic partially observable environments. The problems can be formulated as partially-observable Markov decision processes (POMDPs). Exact solutions of POMDP problems are difficult to find due to the computational complexity, so many approximate solutions are proposed instead. These POMDP solvers tend to generate an approximate policy at planning time and execute the policy without any change at run time. Our approaches will monitor the execution of the initial approximate policy and perform plan modification procedure to improve the policy’s quality at run time. This thesis considers two approximate POMDP solvers. One is a translation-based POMDP solver which converts a subclass of POMDP, called quasi-deterministic POMDP (QDET-POMDP) problems into classical planning problems or Markov decision processes (MDPs). The resulting approximate solution is either a contingency plan or an MDP policy that requires full observability of the world at run time. The other is a point-based POMDP solver which generates an approximate policy by utilizing sampling techniques. Study of the algorithms in simulation has shown that our execution monitoring approaches can improve the approximate POMDP solvers overall performance in terms of plan quality, plan generation time and plan execution time.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA75 Electronic computers. Computer science