151,211 research outputs found
SHOP2: An HTN Planning System
The SHOP2 planning system received one of the awards for distinguished
performance in the 2002 International Planning Competition. This paper
describes the features of SHOP2 which enabled it to excel in the competition,
especially those aspects of SHOP2 that deal with temporal and metric planning
domains
Reconfigurable Decorated PT Nets with Inhibitor Arcs and Transition Priorities
In this paper we deal with additional control structures for decorated PT
Nets. The main contribution are inhibitor arcs and priorities. The first ensure
that a marking can inhibit the firing of a transition. Inhibitor arcs force
that the transition may only fire when the place is empty. an order of
transitions restrict the firing, so that an transition may fire only if it has
the highest priority of all enabled transitions. This concept is shown to be
compatible with reconfigurable Petri nets
An Adaptive Locally Connected Neuron Model: Focusing Neuron
This paper presents a new artificial neuron model capable of learning its
receptive field in the topological domain of inputs. The model provides
adaptive and differentiable local connectivity (plasticity) applicable to any
domain. It requires no other tool than the backpropagation algorithm to learn
its parameters which control the receptive field locations and apertures. This
research explores whether this ability makes the neuron focus on informative
inputs and yields any advantage over fully connected neurons. The experiments
include tests of focusing neuron networks of one or two hidden layers on
synthetic and well-known image recognition data sets. The results demonstrated
that the focusing neurons can move their receptive fields towards more
informative inputs. In the simple two-hidden layer networks, the focusing
layers outperformed the dense layers in the classification of the 2D spatial
data sets. Moreover, the focusing networks performed better than the dense
networks even when 70 of the weights were pruned. The tests on
convolutional networks revealed that using focusing layers instead of dense
layers for the classification of convolutional features may work better in some
data sets.Comment: 45 pages, a national patent filed, submitted to Turkish Patent
Office, No: -2017/17601, Date: 09.11.201
Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
In this paper, we introduce a novel method to interpret recurrent neural
networks (RNNs), particularly long short-term memory networks (LSTMs) at the
cellular level. We propose a systematic pipeline for interpreting individual
hidden state dynamics within the network using response characterization
methods. The ranked contribution of individual cells to the network's output is
computed by analyzing a set of interpretable metrics of their decoupled step
and sinusoidal responses. As a result, our method is able to uniquely identify
neurons with insightful dynamics, quantify relationships between dynamical
properties and test accuracy through ablation analysis, and interpret the
impact of network capacity on a network's dynamical distribution. Finally, we
demonstrate generalizability and scalability of our method by evaluating a
series of different benchmark sequential datasets
Search algorithms as a framework for the optimization of drug combinations
Combination therapies are often needed for effective clinical outcomes in the
management of complex diseases, but presently they are generally based on
empirical clinical experience. Here we suggest a novel application of search
algorithms, originally developed for digital communication, modified to
optimize combinations of therapeutic interventions. In biological experiments
measuring the restoration of the decline with age in heart function and
exercise capacity in Drosophila melanogaster, we found that search algorithms
correctly identified optimal combinations of four drugs with only one third of
the tests performed in a fully factorial search. In experiments identifying
combinations of three doses of up to six drugs for selective killing of human
cancer cells, search algorithms resulted in a highly significant enrichment of
selective combinations compared with random searches. In simulations using a
network model of cell death, we found that the search algorithms identified the
optimal combinations of 6-9 interventions in 80-90% of tests, compared with
15-30% for an equivalent random search. These findings suggest that modified
search algorithms from information theory have the potential to enhance the
discovery of novel therapeutic drug combinations. This report also helps to
frame a biomedical problem that will benefit from an interdisciplinary effort
and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Learning what matters - Sampling interesting patterns
In the field of exploratory data mining, local structure in data can be
described by patterns and discovered by mining algorithms. Although many
solutions have been proposed to address the redundancy problems in pattern
mining, most of them either provide succinct pattern sets or take the interests
of the user into account-but not both. Consequently, the analyst has to invest
substantial effort in identifying those patterns that are relevant to her
specific interests and goals. To address this problem, we propose a novel
approach that combines pattern sampling with interactive data mining. In
particular, we introduce the LetSIP algorithm, which builds upon recent
advances in 1) weighted sampling in SAT and 2) learning to rank in interactive
pattern mining. Specifically, it exploits user feedback to directly learn the
parameters of the sampling distribution that represents the user's interests.
We compare the performance of the proposed algorithm to the state-of-the-art in
interactive pattern mining by emulating the interests of a user. The resulting
system allows efficient and interleaved learning and sampling, thus
user-specific anytime data exploration. Finally, LetSIP demonstrates favourable
trade-offs concerning both quality-diversity and exploitation-exploration when
compared to existing methods.Comment: PAKDD 2017, extended versio
Subtyping for Hierarchical, Reconfigurable Petri Nets
Hierarchical Petri nets allow a more abstract view and reconfigurable Petri
nets model dynamic structural adaptation. In this contribution we present the
combination of reconfigurable Petri nets and hierarchical Petri nets yielding
hierarchical structure for reconfigurable Petri nets. Hierarchies are
established by substituting transitions by subnets. These subnets are
themselves reconfigurable, so they are supplied with their own set of rules.
Moreover, global rules that can be applied in all of the net, are provided
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