219 research outputs found
Efficient Implementation of the Plan Graph in STAN
STAN is a Graphplan-based planner, so-called because it uses a variety of
STate ANalysis techniques to enhance its performance. STAN competed in the
AIPS-98 planning competition where it compared well with the other competitors
in terms of speed, finding solutions fastest to many of the problems posed.
Although the domain analysis techniques STAN exploits are an important factor
in its overall performance, we believe that the speed at which STAN solved the
competition problems is largely due to the implementation of its plan graph.
The implementation is based on two insights: that many of the graph
construction operations can be implemented as bit-level logical operations on
bit vectors, and that the graph should not be explicitly constructed beyond the
fix point. This paper describes the implementation of STAN's plan graph and
provides experimental results which demonstrate the circumstances under which
advantages can be obtained from using this implementation
Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan
This paper reviews the connections between Graphplan's planning-graph and the
dynamic constraint satisfaction problem and motivates the need for adapting CSP
search techniques to the Graphplan algorithm. It then describes how explanation
based learning, dependency directed backtracking, dynamic variable ordering,
forward checking, sticky values and random-restart search strategies can be
adapted to Graphplan. Empirical results are provided to demonstrate that these
augmentations improve Graphplan's performance significantly (up to 1000x
speedups) on several benchmark problems. Special attention is paid to the
explanation-based learning and dependency directed backtracking techniques as
they are empirically found to be most useful in improving the performance of
Graphplan
Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning
In Verification and in (optimal) AI Planning, a successful method is to
formulate the application as boolean satisfiability (SAT), and solve it with
state-of-the-art DPLL-based procedures. There is a lack of understanding of why
this works so well. Focussing on the Planning context, we identify a form of
problem structure concerned with the symmetrical or asymmetrical nature of the
cost of achieving the individual planning goals. We quantify this sort of
structure with a simple numeric parameter called AsymRatio, ranging between 0
and 1. We run experiments in 10 benchmark domains from the International
Planning Competitions since 2000; we show that AsymRatio is a good indicator of
SAT solver performance in 8 of these domains. We then examine carefully crafted
synthetic planning domains that allow control of the amount of structure, and
that are clean enough for a rigorous analysis of the combinatorial search
space. The domains are parameterized by size, and by the amount of structure.
The CNFs we examine are unsatisfiable, encoding one planning step less than the
length of the optimal plan. We prove upper and lower bounds on the size of the
best possible DPLL refutations, under different settings of the amount of
structure, as a function of size. We also identify the best possible sets of
branching variables (backdoors). With minimum AsymRatio, we prove exponential
lower bounds, and identify minimal backdoors of size linear in the number of
variables. With maximum AsymRatio, we identify logarithmic DPLL refutations
(and backdoors), showing a doubly exponential gap between the two structural
extreme cases. The reasons for this behavior -- the proof arguments --
illuminate the prototypical patterns of structure causing the empirical
behavior observed in the competition benchmarks
The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables
Planning with numeric state variables has been a challenge for many years,
and was a part of the 3rd International Planning Competition (IPC-3). Currently
one of the most popular and successful algorithmic techniques in STRIPS
planning is to guide search by a heuristic function, where the heuristic is
based on relaxing the planning task by ignoring the delete lists of the
available actions. We present a natural extension of ``ignoring delete lists''
to numeric state variables, preserving the relevant theoretical properties of
the STRIPS relaxation under the condition that the numeric task at hand is
``monotonic''. We then identify a subset of the numeric IPC-3 competition
language, ``linear tasks'', where monotonicity can be achieved by
pre-processing. Based on that, we extend the algorithms used in the heuristic
planning system FF to linear tasks. The resulting system Metric-FF is,
according to the IPC-3 results which we discuss, one of the two currently most
efficient numeric planners
The FF Planning System: Fast Plan Generation Through Heuristic Search
We describe and evaluate the algorithmic techniques that are used in the FF
planning system. Like the HSP system, FF relies on forward state space search,
using a heuristic that estimates goal distances by ignoring delete lists.
Unlike HSP's heuristic, our method does not assume facts to be independent. We
introduce a novel search strategy that combines hill-climbing with systematic
search, and we show how other powerful heuristic information can be extracted
and used to prune the search space. FF was the most successful automatic
planner at the recent AIPS-2000 planning competition. We review the results of
the competition, give data for other benchmark domains, and investigate the
reasons for the runtime performance of FF compared to HSP
Distributed coordination in unstructured intelligent agent societies
Current research on multi-agent coordination and distributed problem
solving is still not robust or scalable enough to build large real-world
collaborative agent societies because it relies on either centralised components
with full knowledge of the domain or pre-defined social structures.
Our approach allows overcoming these limitations by using
a generic coordination framework for distributed problem solving on
totally unstructured environments that enables each agent to decompose
problems into sub-problems, identify those which it can solve
and search for other agents to delegate the sub-problems for which it
does not have the necessary knowledge or resources. Regarding the
problem decomposition process, we have developed two distributed
versions of the Graphplan planning algorithm. To allow an agent
to discover other agents with the necessary skills for dealing with
unsolved sub-problems, we have created two peer-to-peer search algorithms
that build and maintain a semantic overlay network that
connects agents relying on dependency relationships, which improves
future searches. Our approach was evaluated using two different scenarios,
which allowed us to conclude that it is efficient, scalable and
robust, allowing the coordinated distributed solving of complex problems
in unstructured environments without the unacceptable assumptions
of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuÃda de problemas não são suficientemente robustas ou escaláveis
para criar sociedades de agentes colaborativos uma vez que assentam
ou em componentes centralizados com total conhecimento do
domÃnio ou em estruturas sociais pré-definidas. A nossa abordagem
permite superar estas limitações através da utilização de um algoritmo
genérico de coordenação de resolução distribuÃda de problemas
em ambientes totalmente não estruturados, o qual permite a cada
agente decompor problemas em sub-problemas, identificar aqueles que
consegue resolver e procurar outros agentes a quem delegar os subproblemas
para os quais não tem conhecimento suficiente. Para a
decomposição de problemas, criámos duas versões distribuÃdas do algoritmo
de planeamento Graphplan. Para procurar os agentes com as
capacidades necessárias à resolução das partes não resolvidas do problema,
criámos dois algoritmos de procura que constroem e mantêm
uma camada de rede semântica que relaciona agentes dependentes
com o fim de facilitar as procuras. A nossa abordagem foi avaliada
em dois cenários diferentes, o que nos permitiu concluir que ´e uma
abordagem eficiente, escalável e robusta, possibilitando a resolução
distribuÃda e coordenada de problemas complexos em ambientes não
estruturados sem os pressupostos inaceitáveis em que assentava o trabalho
feito até agora
Loosely Coupled Formulations for Automated Planning: An Integer Programming Perspective
We represent planning as a set of loosely coupled network flow problems,
where each network corresponds to one of the state variables in the planning
domain. The network nodes correspond to the state variable values and the
network arcs correspond to the value transitions. The planning problem is to
find a path (a sequence of actions) in each network such that, when merged,
they constitute a feasible plan. In this paper we present a number of integer
programming formulations that model these loosely coupled networks with varying
degrees of flexibility. Since merging may introduce exponentially many ordering
constraints we implement a so-called branch-and-cut algorithm, in which these
constraints are dynamically generated and added to the formulation when needed.
Our results are very promising, they improve upon previous planning as integer
programming approaches and lay the foundation for integer programming
approaches for cost optimal planning
Semantics-aware planning methodology for automatic web service composition
Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed
- …