1,721,042 research outputs found
Generating Abstractions from Static Domain Analysis
No full textThis paper addresses the problem of how to
implement a proactive behavior according to a two-tiered (i.e.,
both theoretical and pragmatic) perspective. Theoretically, we
claim that abstraction must be used to render agents able to solve
complex problems. Pragmatically, we illustrate a technique
devised to generate abstract spaces starting from a “ground”
description of the domain being modeled
A Critical Look at the Abstraction Based on Macro-Operators
Full text linkAbstraction can be an effective technique for dealing with
the complexity of planning tasks. This paper is aimed at assessing and
identifying in which cases abstraction can actually speed-up the overall
search. In fact, it is well known that the impact of abstraction on the
time spent to search for a solution of a planning problem can be positive
or negative, depending on several factors -including the number of objects
defined in the domain, the branching factor, and the plan length.
Experimental results highlight the role of such aspects on the overall performance
of an algorithm that performs the search at the ground-level
only, and compares them with the ones obtained by enforcing abstraction
Experimenting Abstraction Mechanisms Through an Agent-Based Hierarchical Planner
No full textIn this paper, an agent-based architecture devised
to perform experiments on hierarchical planning is described.
The planning activity results from the interaction of a
community of agents, some of them being explicitly devoted to
embed one or more existing planners. The proposed
architecture allows to exploit the characteristics of any external
planner, under the hypothesis that a suitable wrapper –in form
of planning agent– is provided. An implementation of the
architecture, able to embed one planner of the graphplan
family, has been used to directly assess whether or not
abstraction mechanisms can help to reduce the time complexity
of the search on specific domains. Some preliminary
experiments are reported, focusing on problems taken from the
AIPS 2002, 2000 and 1998 planning competitions. Comparative
results, obtained by assessing the performances of the selected
planner (used first in a stand-alone configuration and then
embedded into the proposed multi-agent architecture), put into
evidence that abstraction may significantly speed up the search
Implementing Adaptive Capabilities on Agents that Act in a Dynamic Environment
No full textActing in a dynamic environment is a complex task that requires several issues to be investigated. In this paper, a lifecycle
for implementing adaptive capabilities on intelligent agents is proposed, which integrates planning and learning
within a hierarchical framework. The integration between planning and learning is achieved by an agent architecture
explicitly designed for supporting abstraction. Planning is performed by adopting a hierarchical interleaved planning
and execution approach. Learning is performed by exploiting a chunking technique on successful plans. A suitable
feedforward neural network selects relevant chunks used to identify new abstract operators. Due to the dependency
between abstract operators and already-solved planning problems, each agent is able to develop its own abstract layer,
thus achieving an individual adaptation to the given environment
An Extension to PDDL for Hierarchical Planning
No full textThis paper describes an extension to PDDL, devised to
support hierarchical planning. The proposed syntactic
notation should be considered as an initial suggestion,
headed at promoting a discussion about how the standard
PDDL can be extended to represent abstraction
hierarchies
VOLMAP: a Large Scale Benchmark for Volume Mappings to Simple Base Domains
Full text linkCorrespondences between geometric domains (mappings) are ubiquitous in computer graphics and engineering, both for a variety of downstream applications and as core building blocks for higher level algorithms. In particular, mapping a shape to a convex or star-shaped domain with simple geometry is a fundamental module in existing pipelines for mesh generation, solid texturing, generation of shape correspondences, advanced manufacturing etc. For the case of surfaces, computing such a mapping with guarantees of injectivity is a solved problem. Conversely, robust algorithms for the generation of injective volume mappings to simple polytopes are yet to be found, making this a fundamental open problem in volume mesh processing. VOLMAP is a large scale benchmark aimed to support ongoing research in volume mapping algorithms. The dataset contains 4.7K tetrahedral meshes, whose boundary vertices are mapped to a variety of simple domains, either convex or star-shaped. This data constitutes the input for candidate algorithms, which are then required to position interior vertices in the domain to obtain a volume map. Overall, this yields more than 22K alternative test cases. VOLMAP also comprises tools to process this data, analyze the resulting maps, and extend the dataset with new meshes, boundary maps and base domains. This article provides a brief overview of the field, discussing its importance and the lack of effective techniques. We then introduce both the dataset and its major features. An example of comparative analysis between two existing methods is also present
A Parametric Hierarchical Planner for Experimenting Abstraction Techniques
Full text linkThis paper presents a parametric system, devised
and implemented to perform hierarchical planning
by delegating the actual search to an external
planner (the "parameter") at any level of abstraction,
including the ground one. Aimed at
giving a better insight of whether or not the exploitation
of abstract spaces can be used for
solving complex planning problems, comparisons
have been made between instances of the
hierarchical planner and their non hierarchical
counterparts. To improve the significance of the
results, three different planners have been selected
and used while performing experiments.
To facilitate the setting of experimental environments,
a novel semi-automatic technique,
used to generate abstraction hierarchies starting
from ground-level domain descriptions, is also
described
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