181,708 research outputs found
Towards rule-based visual programming of generic visual systems
This paper illustrates how the diagram programming language DiaPlan can be
used to program visual systems. DiaPlan is a visual rule-based language that is
founded on the computational model of graph transformation. The language
supports object-oriented programming since its graphs are hierarchically
structured. Typing allows the shape of these graphs to be specified recursively
in order to increase program security. Thanks to its genericity, DiaPlan allows
to implement systems that represent and manipulate data in arbitrary diagram
notations. The environment for the language exploits the diagram editor
generator DiaGen for providing genericity, and for implementing its user
interface and type checker.Comment: 15 pages, 16 figures contribution to the First International Workshop
on Rule-Based Programming (RULE'2000), September 19, 2000, Montreal, Canad
Using Graph Transformations and Graph Abstractions for Software Verification
In this paper we describe our intended approach for the verification of software written in imperative programming languages. We base our approach on model checking of graph transition systems, where each state is a graph and the transitions are specified by graph transformation rules. We believe that graph transformation is a very suitable technique to model the execution semantics of languages with dynamic memory allocation. Furthermore, such representation allows us to investigate the use of graph abstractions, which can mitigate the combinatorial explosion inherent to model checking. In addition to presenting our planned approach, we reason about its feasibility, and, by providing a brief comparison to other existing methods, we highlight the benefits and drawbacks that are expected
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
This paper discusses a system that accelerates reinforcement learning by
using transfer from related tasks. Without such transfer, even if two tasks are
very similar at some abstract level, an extensive re-learning effort is
required. The system achieves much of its power by transferring parts of
previously learned solutions rather than a single complete solution. The system
exploits strong features in the multi-dimensional function produced by
reinforcement learning in solving a particular task. These features are stable
and easy to recognize early in the learning process. They generate a
partitioning of the state space and thus the function. The partition is
represented as a graph. This is used to index and compose functions stored in a
case base to form a close approximation to the solution of the new task.
Experiments demonstrate that function composition often produces more than an
order of magnitude increase in learning rate compared to a basic reinforcement
learning algorithm
Range-based techniques for discovering optimality and analyzing scaling relationships in neuromechanical systems
In this paper, a method for decoupling the neuromuscular function of a set of limbs from the role morphology plays in regulating the performance of an activity is introduced. This method is based on two previous methods: the rescaled range analysis specific to time series data, and the use of scaling laws. A review of the literature suggests that limb geometry can either facilitate or constrain performance as measured experimentally. Whether limb geometry is facilitatory or acts as a constraint depends on the size differential between arm morphology and the underlying muscle. "Changes in size and shape" are theoretically extrapolations of morphological geometry to other members of a population or species, to other species, or to technological manipulations of an individual via prosthetic devices. Three datasets are analyzed using the range-based method and a Monte-Carlo simulation, and are used to test the various ways of executing this analysis. It was found that when performance is kept stable but limb size and shape is scaled by a factor of .25, the greatest gain in performance results. It was also found that introducing force-based perturbations results in 'shifts' in the body geometry/performance relationship. While results such as this could be interpreted as a statistical artifact, the non-linear rise within a measurement class and linear decrease between measurement classes suggests an effect of scale in the optimality of this relationship. Overall, range-based techniques allow for the simulation and modeling of myriad changes in phenotype that result from biological and technological manipulation
Unfolding Shape Graphs
Shape graphs have been introduced in [Ren04a, Ren04b] as an abstraction to be used in model checking object oriented software, where states of the system are represented as graphs. Intuitively, the graphs modeling the states represent the structure of objects dynamically allocated in the heap. State transitions are then generated by applying graph transformation rules corresponding to the statements of the program. Since the state space of such systems is potentially unbounded, the graphs representing the states are abstracted by shape graphs. Graph transformation systems may be analyzed [BCK01, BK02] by constructing finite structures that approximate their behaviour with arbitrary accuracy, by using techniques developed in the context of Petri nets. The approach of [BK02] is to construct a chain of finite under-approximations of the Winskel’s style unfolding of a graph grammar, as well as a chain of finite over-approximations of the unfolding, where both chains converge to the full unfolding. The approximations may then be used to check properties of the underlying graph transformation system. We apply this technique to approximate the behaviour of systems represented by shape graphs and graph tranformation rules
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