8 research outputs found
Exploiting Block Deordering for Improving Planners Efficiency
Capturing and exploiting structural knowledge of
planning problems has shown to be a successful
strategy for making the planning process more ef-
ficient. Plans can be decomposed into its constituent
coherent subplans, called blocks, that encapsulate
some effects and preconditions, reducing
interference and thus allowing more deordering
of plans. According to the nature of blocks, they
can be straightforwardly transformed into useful
macro-operators (shortly, “macros”). Macros are
well known and widely studied kind of structural
knowledge because they can be easily encoded in
the domain model and thus exploited by standard
planning engines.
In this paper, we introduce a method, called
BLOMA, that learns domain-specific macros from
plans, decomposed into “macro-blocks” which are
extensions of blocks, utilising structural knowledge
they capture. In contrast to existing macro learning
techniques, macro-blocks are often able to capture
high-level activities that form a basis for useful
longer macros (i.e. those consisting of more original
operators). Our method is evaluated by using
the IPC benchmarks with state-of-the-art planning
engines, and shows considerable improvement in
many cases
The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning
This paper presents GRT, a domain-independent heuristic planning system for
STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase,
it estimates the distance between each fact and the goals of the problem, in a
backward direction. Then, in the search phase, these estimates are used in
order to further estimate the distance between each intermediate state and the
goals, guiding so the search process in a forward direction and on a best-first
basis. The paper presents the benefits from the adoption of opposite directions
between the preprocessing and the search phases, discusses some difficulties
that arise in the pre-processing phase and introduces techniques to cope with
them. Moreover, it presents several methods of improving the efficiency of the
heuristic, by enriching the representation and by reducing the size of the
problem. Finally, a method of overcoming local optimal states, based on domain
axioms, is proposed. According to it, difficult problems are decomposed into
easier sub-problems that have to be solved sequentially. The performance
results from various domains, including those of the recent planning
competitions, show that GRT is among the fastest planners
Technological roadmap on AI planning and scheduling
At the beginning of the new century, Information Technologies had become basic and indispensable
constituents of the production and preparation processes for all kinds of goods and services and
with that are largely influencing both the working and private life of nearly every citizen. This
development will continue and even further grow with the continually increasing use of the Internet
in production, business, science, education, and everyday societal and private undertaking.
Recent years have shown, however, that a dramatic enhancement of software capabilities is required,
when aiming to continuously provide advanced and competitive products and services in all these
fast developing sectors. It includes the development of intelligent systems – systems that are more
autonomous, flexible, and robust than today’s conventional software.
Intelligent Planning and Scheduling is a key enabling technology for intelligent systems. It has
been developed and matured over the last three decades and has successfully been employed for a
variety of applications in commerce, industry, education, medicine, public transport, defense, and
government.
This document reviews the state-of-the-art in key application and technical areas of Intelligent Planning
and Scheduling. It identifies the most important research, development, and technology transfer
efforts required in the coming 3 to 10 years and shows the way forward to meet these challenges in
the short-, medium- and longer-term future.
The roadmap has been developed under the regime of PLANET – the European Network of Excellence
in AI Planning. This network, established by the European Commission in 1998, is the co-ordinating
framework for research, development, and technology transfer in the field of Intelligent Planning and
Scheduling in Europe.
A large number of people have contributed to this document including the members of PLANET non-
European international experts, and a number of independent expert peer reviewers. All of them are
acknowledged in a separate section of this document.
Intelligent Planning and Scheduling is a far-reaching technology. Accepting the challenges and progressing
along the directions pointed out in this roadmap will enable a new generation of intelligent
application systems in a wide variety of industrial, commercial, public, and private sectors
Relational Envelope-based Planning
This thesis proposes a synthesis of logic and probability for solving stochastic sequential decision-making problems. We address two main questions: How can we take advantage of logical structure to speed up planning in a principled way? And, how can probability inform the production of a more robust, yet still compact, policy? We can take as inspiration a mobile robot acting in the world: it is faced with a varied amount ofsensory data and uncertainty in its action outcomes. Or, consider a logistics planning system: it must deliver a large number of objects to the right place at the right time. Many interesting sequential decision-making domains involve large statespaces, large stochastic action sets, and time pressure to act. In this work, we show how structured representations of the environment's dynamics can constrain and speed up the planning process. We start with a problem domain described in a probabilistic logical description language.Our technique is based on, first, identifying the most parsimonious representation that permits solution of the described problem. Next, we take advantage of the structured problem description to dynamically partition the action space into a set of equivalence classes with respect to this minimal representation. The partitioned action space results in fewer distinctactions. This technique can yield significant gains in planning efficiency.Next, we develop an anytime technique to elaborate on this initial plan. Our approach uses the envelope MDP framework, which creates a Markov decision process out of a subset of the possible state space. This strategy lets an agent begin acting quicklywithin a restricted part of the full state space, as informed by the original plan,and to judiciously expand its envelope as resources permit.Finally, we show how the representation space itself can be elaborated within the anytime framework. This approach balances the need to respond to time-pressure and to produce the most robust policies possible. We present experimental results in some synthetic planning domains and in a simulated military logistics domain
Using Plan Decomposition for Continuing Plan Optimisation and Macro Generation
This thesis addresses three problems in the field of classical AI planning: decomposing
a plan into meaningful subplans, continuing plan quality optimisation, and
macro generation for efficient planning. The importance and difficulty of each of
these problems is outlined below.
(1) Decomposing a plan into meaningful subplans can facilitate a number of postplan
generation tasks, including plan quality optimisation and macro generation
– the two key concerns of this thesis. However, conventional plan decomposition
techniques are often unable to decompose plans because they consider dependencies
among steps, rather than subplans.
(2) Finding high quality plans for large planning problems is hard. Planners that
guarantee optimal, or bounded suboptimal, plan quality often cannot solve them In
one experiment with the Genome Edit Distance domain optimal planners solved only
11.5% of problems. Anytime planners promise a way to successively produce better
plans over time. However, current anytime planners tend to reach a limit where they
stop finding any further improvement, and the plans produced are still very far from
the best possible. In the same experiment, the LAMA anytime planner solved all
problems but found plans whose average quality is 1.57 times worse than the best
known.
(3) Finding solutions quickly or even finding any solution for large problems
within some resource constraint is also difficult. The best-performing planner in
the 2014 international planning competition still failed to solve 29.3% of problems.
Re-engineering a domain model by capturing and exploiting structural knowledge
in the form of macros has been found very useful in speeding up planners. However,
existing planner independent macro generation techniques often fail to capture
some promising macro candidates because the constituent actions are not found in
sequence in the totally ordered training plans.
This thesis contributes to plan decomposition by developing a new plan deordering
technique, named block deordering, that allows two subplans to be unordered
even when their constituent steps cannot. Based on the block-deordered
plan, this thesis further contributes to plan optimisation and macro generation, and
their implementations in two systems, named BDPO2 and BloMa. Key to BDPO2
is a decomposition into subproblems of improving parts of the current best plan,
rather than the plan as a whole. BDPO2 can be seen as an application of the large
neighbourhood search strategy to planning. We use several windowing strategies to
extract subplans from the block deordering of the current plan, and on-line learning
for applying the most promising subplanners to the most promising subplans.
We demonstrate empirically that even starting with the best plans found by other
means, BDPO2 is still able to continue improving plan quality, and often produces better plans than other anytime planners when all are given enough runtime. BloMa
uses an automatic planner independent technique to extract and filter “self-containe”
subplans as macros from the block deordered training plans. These macros represent
important longer activities useful to improve planners coverage and efficiency
compared to the traditional macro generation approaches