@MASTERSTHESIS\{IMM2013-06557,
    author       = "D. Svendsen",
    title        = "Implementation of Conditional Epistemic Planning",
    year         = "2013",
    school       = "Technical University of Denmark, {DTU} Compute, {E-}mail: compute@compute.dtu.dk",
    address      = "Matematiktorvet, Building 303{-B,} {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    url          = "http://www.compute.dtu.dk/English.aspx",
    abstract     = "Automated planning is an area of Artificial Intelligence that studies reasoning about acting, an abstract deliberation process that chooses and organizes action by anticipating outcomes [1]. Classical planning deals with restricted state transition systems. They are deterministic, static, finite and fully observable and with restricted goals and implicit time [1]. In this project we go beyond classical planning by extending on the restrictions of classical planning by considering partial observability (not the entire world is known) and non-determinism (applying the same action to the same state, might not always yield the same result). Recently research [2] has shown that planning under partial observability and non-determinism fits naturally within the theory of dynamic epistemic logic (DEL). In [2], an algorithm for planning under partial observability and non-determinism is provided based on the {DEL} framework. The primary goal of this project is to implement the algorithm of the paper. Implementation of the planning algorithm of [2] involves parsing of models, computing product updates for states, generating and-or-trees via the tree expansion rule and model checking to check for completed goal formulas. In addition to implementing the algorithm, the project will seek to come up with interesting planning domains that fit into the framework and can showcase {DEL-}based planning and the implementation of it. Furthermore, depending on early successes, implementing the basic steps of the algorithm, several futher avenues can be pursued; these include: Implementation of a model based planner (MBP), plan validation, implementing tools for creating bigger examples in NuPDDL or similar language."
}