Translation Algorithm for Negative Literals in Conformant Planning
نویسندگان
چکیده
The encoded negative literals in a conformant planning task will result in increasing state spaces. Getting a compact representation of state spaces is one of the most important issues in conformant planning. In this paper, a translation algorithm for negative literals is proposed to reduce the state spaces in a conformant planning task. The relationship between encoded literals is analyzed in detail. Based on the one-of relaxation technique in domain language, the algorithm is used to express the uncertain initial states and action effects in conformant planning. It converts formula one-of into a set of mutually exclusive literals with the relationship of mutual. The experiment study shows the efficiency of the proposed algorithm in pruning the state space in conformant planning tasks.
منابع مشابه
Compiling Uncertainty Away in Conformant Planning Problems with Bounded Width
Conformant planning is the problem of finding a sequence of actions for achieving a goal in the presence of uncertainty in the initial state or action effects. The problem has been approached as a path-finding problem in belief space where good belief representations and heuristics are critical for scaling up. In this work, a different formulation is introduced for conformant problems with dete...
متن کاملFrom Conformant into Classical Planning: Efficient Translations that May Be Complete Too
Focusing on the computation of conformant plans whose verification can be done efficiently, we have recently proposed a polynomial scheme for mapping conformant problems P with deterministic actions into classical problemsK(P ). The scheme is sound as the classical plans are all conformant, but is incomplete as the converse relation does not always hold. In this paper, we extend this work and c...
متن کاملPlanning in Belief Space with a Labelled Uncertainty Graph
Planning in belief space with a Labelled Uncertainty Graph, LUG, is an approach that uses a very compact planning graph to guide search in the space of belief states to construct conformant and contingent plans. A conformant plan is a plan that transitions (without sensing) all possible initial states through possibly non-deterministic actions to a goal state. A contingent plan adds the ability...
متن کاملApplying Marginal MAP Search to Probabilistic Conformant Planning: Initial Results
In this position paper, we present our current progress in applying marginal MAP algorithms for solving the conformant planning problems. Conformant planning problem is formulated as probabilistic inference in graphical models compiled from relational PPDDL domains. The translation from PPDDL into Dynamic Bayesian Network is developed by mapping the SAT encoding of the ground PPDDL into factore...
متن کاملApplying Search Based Probabilistic Inference Algorithms to Probabilistic Conformant Planning: Preliminary Results
Probabilistic conformant planning problems can be solved by probabilistic inference algorithms after translating their PPDDL specifications into graphical models. We present two translation schemes that convert probabilistic conformant planning problems as graphical models. The first encoding is based on the probabilistic extension of the serial encoding of PDDL in SatPlan, and the second encod...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- JSW
دوره 8 شماره
صفحات -
تاریخ انتشار 2013