1,051 research outputs found
Conformant Planning via Symbolic Model Checking
We tackle the problem of planning in nondeterministic domains, by presenting
a new approach to conformant planning. Conformant planning is the problem of
finding a sequence of actions that is guaranteed to achieve the goal despite
the nondeterminism of the domain. Our approach is based on the representation
of the planning domain as a finite state automaton. We use Symbolic Model
Checking techniques, in particular Binary Decision Diagrams, to compactly
represent and efficiently search the automaton. In this paper we make the
following contributions. First, we present a general planning algorithm for
conformant planning, which applies to fully nondeterministic domains, with
uncertainty in the initial condition and in action effects. The algorithm is
based on a breadth-first, backward search, and returns conformant plans of
minimal length, if a solution to the planning problem exists, otherwise it
terminates concluding that the problem admits no conformant solution. Second,
we provide a symbolic representation of the search space based on Binary
Decision Diagrams (BDDs), which is the basis for search techniques derived from
symbolic model checking. The symbolic representation makes it possible to
analyze potentially large sets of states and transitions in a single
computation step, thus providing for an efficient implementation. Third, we
present CMBP (Conformant Model Based Planner), an efficient implementation of
the data structures and algorithm described above, directly based on BDD
manipulations, which allows for a compact representation of the search layers
and an efficient implementation of the search steps. Finally, we present an
experimental comparison of our approach with the state-of-the-art conformant
planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems
from the distribution packages of these systems, plus other problems defined to
stress a number of specific factors. Our approach appears to be the most
effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all
the problems where a comparison is possible, CMBP outperforms its competitors,
sometimes by orders of magnitude
Conformant Planning as a Case Study of Incremental QBF Solving
We consider planning with uncertainty in the initial state as a case study of
incremental quantified Boolean formula (QBF) solving. We report on experiments
with a workflow to incrementally encode a planning instance into a sequence of
QBFs. To solve this sequence of incrementally constructed QBFs, we use our
general-purpose incremental QBF solver DepQBF. Since the generated QBFs have
many clauses and variables in common, our approach avoids redundancy both in
the encoding phase and in the solving phase. Experimental results show that
incremental QBF solving outperforms non-incremental QBF solving. Our results
are the first empirical study of incremental QBF solving in the context of
planning and motivate its use in other application domains.Comment: added reference to extended journal article; revision (camera-ready,
to appear in the proceedings of AISC 2014, volume 8884 of LNAI, Springer
Efficient Open World Reasoning for Planning
We consider the problem of reasoning and planning with incomplete knowledge
and deterministic actions. We introduce a knowledge representation scheme
called PSIPLAN that can effectively represent incompleteness of an agent's
knowledge while allowing for sound, complete and tractable entailment in
domains where the set of all objects is either unknown or infinite. We present
a procedure for state update resulting from taking an action in PSIPLAN that is
correct, complete and has only polynomial complexity. State update is performed
without considering the set of all possible worlds corresponding to the
knowledge state. As a result, planning with PSIPLAN is done without direct
manipulation of possible worlds. PSIPLAN representation underlies the PSIPOP
planning algorithm that handles quantified goals with or without exceptions
that no other domain independent planner has been shown to achieve. PSIPLAN has
been implemented in Common Lisp and used in an application on planning in a
collaborative interface.Comment: 39 pages, 13 figures. to appear in Logical Methods in Computer
Scienc
Engineering a Conformant Probabilistic Planner
We present a partial-order, conformant, probabilistic planner, Probapop which
competed in the blind track of the Probabilistic Planning Competition in IPC-4.
We explain how we adapt distance based heuristics for use with probabilistic
domains. Probapop also incorporates heuristics based on probability of success.
We explain the successes and difficulties encountered during the design and
implementation of Probapop
A Logic Programming Approach to Knowledge-State Planning: Semantics and Complexity
We propose a new declarative planning language, called K, which is based on
principles and methods of logic programming. In this language, transitions
between states of knowledge can be described, rather than transitions between
completely described states of the world, which makes the language well-suited
for planning under incomplete knowledge. Furthermore, it enables the use of
default principles in the planning process by supporting negation as failure.
Nonetheless, K also supports the representation of transitions between states
of the world (i.e., states of complete knowledge) as a special case, which
shows that the language is very flexible. As we demonstrate on particular
examples, the use of knowledge states may allow for a natural and compact
problem representation. We then provide a thorough analysis of the
computational complexity of K, and consider different planning problems,
including standard planning and secure planning (also known as conformant
planning) problems. We show that these problems have different complexities
under various restrictions, ranging from NP to NEXPTIME in the propositional
case. Our results form the theoretical basis for the DLV^K system, which
implements the language K on top of the DLV logic programming system.Comment: 48 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
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