7 research outputs found
Backwards State-space Reduction for Planning in Dynamic Knowledge Bases
In this paper we address the problem of planning in rich domains, where
knowledge representation is a key aspect for managing the complexity and size
of the planning domain. We follow the approach of Description Logic (DL) based
Dynamic Knowledge Bases, where a state of the world is represented concisely by
a (possibly changing) ABox and a (fixed) TBox containing the axioms, and
actions that allow to change the content of the ABox. The plan goal is given in
terms of satisfaction of a DL query. In this paper we start from a traditional
forward planning algorithm and we propose a much more efficient variant by
combining backward and forward search. In particular, we propose a Backward
State-space Reduction technique that consists in two phases: first, an Abstract
Planning Graph P is created by using the Abstract Backward Planning Algorithm
(ABP), then the abstract planning graph P is instantiated into a corresponding
planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The
advantage is that in the preliminary ABP phase we produce a symbolic plan that
is a pattern to direct the search of the concrete plan. This can be seen as a
kind of informed search where the preliminary backward phase is useful to
discover properties of the state-space that can be used to direct the
subsequent forward phase. We evaluate the effectiveness of our ABP+FPI
algorithm in the reduction of the explored planning domain by comparing it to a
standard forward planning algorithm and applying both of them to a concrete
business case study.Comment: In Proceedings GRAPHITE 2014, arXiv:1407.767
Synthesizing and executing plans in Knowledge and Action Bases
We study plan synthesis for a variant of Knowledge and Action Bases (KABs). KABs have been recently introduced as a rich, dynamic framework where states are full-fledged description logic (DL) knowledge bases (KBs) whose extensional part is manipulated by actions that can introduce new objects from an infinite domain. We show that, in general, plan existence over KABs is undecidable even under severe restrictions. We then focus on the class of state-bounded KABs, for which plan existence is decidable, and we provide sound and complete plan synthesis algorithms, through a novel combination of techniques based on standard planning, DL query answering, and finite-state abstractions. All results hold for any DL with decidable query answering. We finally show that for lightweight DLs, plan synthesis can be compiled into standard ADL planning. © 2016, CEUR-WS. All rights reserved
Plan Synthesis for Knowledge and Action Bases
We study plan synthesis for a variant of Knowledge and Action Bases (KABs), a rich, dynamic framework, where states are description logic (DL) knowledge bases (KBs) whose extensional part is manipulated by actions that possibly introduce new objects from an infinite domain. We show that plan existence over KABs is undecidable even under severe restrictions. We then focus on state-bounded KABs, a class for which plan existence is decidable, and provide sound and complete plan synthesis algorithms, which combine techniques based on standard planning, DL query answering, and finite-state abstraction. All results hold for any DL with decidable query answering. We finally show that for lightweight DLs, plan synthesis can be compiled into standard ADL planning
Optimizations for Decision Making and Planning in Description Logic Dynamic Knowledge Bases
Abstract. Artifact-centric models for business processes recently raised a lot of attention, as they manage to combine structural (i.e. data related) with dynamical (i.e. process related) aspects in a seamless way. Many frameworks developed under this approach, although, are not built explicitly for planning, one of the most prominent operations related to business processes. In this paper, we try to overcome this by proposing a framework named Dynamic Knowledge Bases, aimed at describing rich business domains through Description Logic-based ontologies, and where a set of actions allows the system to evolve by modifying such ontologies. This framework, by offering action rewriting and knowledge partialization, represents a viable and formal environment to develop decision making and planning techniques for DL-based artifact-centric business domains
Plan Synthesis in Explicit-Input Knowledge and Action Bases
In this Thesis we study plan synthesis for datacentric
domains, where the interest is not only upon
the actions the system performs to reach its desired
goal, but also on how the knowledge defining the
domain evolves with the aforementioned actions.
We first introduce a rich, dynamic framework
named Explicit-input Knowledge and Action Bases
(eKABs), where states are Description Logic (DL)
Knowledge Bases, whose extensional part is manipulated
by actions that possibly introduce new objects
from an infinite domain. We show that plan
existence over eKABs is undecidable even under
severe restrictions.
We then focus on state-bounded eKABs, a class
for which plan existence is decidable, and provide
sound and complete plan synthesis algorithms, which
combine techniques based on standard planning,
DL query answering, and finite-state abstraction.
All results hold for any DL with decidable query
answering.
We finally show that for lightweight DLs, plan
synthesis can be compiled into standard planning,
and we provide two translations: translation to
STRIPS for a restricted version of lightweight DL
eKABs, and translation to ADL for full lightweight
DL eKABs. For the STRIPS setting, we provide an
additional technique to optimize Knowledge Base
satisfiability check inside the translation. We also
provide a technique showing how it is possible to
transform any full lightweight DL eKAB to an equivalent
restricted lightweight DL eKAB
Synthesizing and Executing Plans in Knowledge and Action Bases
Abstract. We study plan synthesis for a variant of Knowledge and Action Bases (KABs). KABs have been recently introduced as a rich, dynamic framework where states are full-fledged description logic (DL) knowledge bases (KBs) whose extensional part is manipulated by actions that can introduce new objects from an infinite domain. We show that, in general, plan existence over KABs is undecidable even under severe restrictions. We then focus on the class of statebounded KABs, for which plan existence is decidable, and we provide sound and complete plan synthesis algorithms, through a novel combination of techniques based on standard planning, DL query answering, and finite-state abstractions. All results hold for any DL with decidable query answering. We finally show that for lightweight DLs, plan synthesis can be compiled into standard ADL planning