3,296 research outputs found
ASAP: An Automatic Algorithm Selection Approach for Planning
Despite the advances made in the last decade in automated planning, no planner out-
performs all the others in every known benchmark domain. This observation motivates
the idea of selecting different planning algorithms for different domains. Moreover, the
planners’ performances are affected by the structure of the search space, which depends
on the encoding of the considered domain. In many domains, the performance of a plan-
ner can be improved by exploiting additional knowledge, for instance, in the form of
macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for
Planning that: (i) for a given domain initially learns additional knowledge, in the form
of macro-operators and entanglements, which is used for creating different encodings
of the given planning domain and problems, and (ii) explores the 2 dimensional space
of available algorithms, defined as encodings–planners couples, and then (iii) selects the
most promising algorithm for optimising either the runtimes or the quality of the solution
plans
Portfolio-based Planning: State of the Art, Common Practice and Open Challenges
In recent years the field of automated planning has significantly
advanced and several powerful domain-independent
planners have been developed. However, none of these systems
clearly outperforms all the others in every known
benchmark domain. This observation motivated the idea of
configuring and exploiting a portfolio of planners to perform
better than any individual planner: some recent planning systems
based on this idea achieved significantly good results in
experimental analysis and International Planning Competitions.
Such results let us suppose that future challenges of the
Automated Planning community will converge on designing
different approaches for combining existing planning algorithms.
This paper reviews existing techniques and provides an exhaustive
guide to portfolio-based planning. In addition, the
paper outlines open issues of existing approaches and highlights
possible future evolution of these techniques
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
Bayesian robot Programming
We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics
Dynamic real-time hierarchical heuristic search for pathfinding.
Movement of Units in Real-Time Strategy (RTS) Games is a non-trivial and challenging task mainly due to three factors which are constraints on CPU and memory usage, dynamicity of the game world, and concurrency. In this paper, we are focusing on finding a novel solution for solving the pathfinding problem in RTS Games for the units which are controlled by the computer. The novel solution combines two AI Planning approaches: Hierarchical Task Network (HTN) and Real-Time Heuristic Search (RHS). In the proposed solution, HTNs are used as a dynamic abstraction of the game map while RHS works as planning engine with interleaving of plan making and action executions. The article provides algorithmic details of the model while the empirical details of the model are obtained by using a real-time strategy game engine called ORTS (Open Real-time Strategy). The implementation of the model and its evaluation methods are in progress however the results of the automatic HTN creation are obtained for a small scale game map
A Planning-based Approach for Music Composition
. Automatic music composition is a fascinating field within computational
creativity. While different Artificial Intelligence techniques have been used
for tackling this task, Planning – an approach for solving complex combinatorial
problems which can count on a large number of high-performance systems and
an expressive language for describing problems – has never been exploited.
In this paper, we propose two different techniques that rely on automated planning
for generating musical structures. The structures are then filled from the bottom
with “raw” musical materials, and turned into melodies. Music experts evaluated
the creative output of the system, acknowledging an overall human-enjoyable
trait of the melodies produced, which showed a solid hierarchical structure and a
strong musical directionality. The techniques proposed not only have high relevance
for the musical domain, but also suggest unexplored ways of using planning
for dealing with non-deterministic creative domains
The 2014 International Planning Competition: Progress and Trends
We review the 2014 International Planning Competition (IPC-2014), the eighth
in a series of competitions starting in 1998. IPC-2014 was held in three separate
parts to assess state-of-the-art in three prominent areas of planning research: the
deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic
part (IPPC). Each part evaluated planning systems in ways that pushed the edge of
existing planner performance by introducing new challenges, novel tasks, or both.
The competition surpassed again the number of competitors than its predecessor,
highlighting the competition’s central role in shaping the landscape of ongoing
developments in evaluating planning systems
Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning
Case-Based planning can fruitfully exploit knowledge
gained by solving a large number of problems, storing
the corresponding solutions in a plan library and reusing
them for solving similar planning problems in the future.
Case-based planning is extremely effective when
similar reuse candidates can be efficiently chosen.
In this paper, we study an innovative technique based
on planning problem features for efficiently retrieving
solved planning problems (and relative plans) from
large plan libraries. A problem feature is a characteristic
of the instance that can be automatically derived from
the problem specification, domain and search space
analyses, and different problem encodings.
Since the use of existing planning features are not always
able to effectively distinguish between problems
within the same planning domain, we introduce a new
class of features.
An experimental analysis in this paper shows that our
features-based retrieval approach can significantly improve
the performance of a state-of-the-art case-based
planning system
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