213 research outputs found
Postponing Branching Decisions
Solution techniques for Constraint Satisfaction and Optimisation Problems
often make use of backtrack search methods, exploiting variable and value
ordering heuristics. In this paper, we propose and analyse a very simple method
to apply in case the value ordering heuristic produces ties: postponing the
branching decision. To this end, we group together values in a tie, branch on
this sub-domain, and defer the decision among them to lower levels of the
search tree. We show theoretically and experimentally that this simple
modification can dramatically improve the efficiency of the search strategy.
Although in practise similar methods may have been applied already, to our
knowledge, no empirical or theoretical study has been proposed in the
literature to identify when and to what extent this strategy should be used.Comment: 11 pages, 3 figure
A min-flow algorithm for Minimal Critical Set detection in Resource Constrained Project Scheduling
AbstractWe propose a min-flow algorithm for detecting Minimal Critical Sets (MCS) in Resource Constrained Project Scheduling Problems (RCPSP). The MCS detection is a fundamental step in the Precedence Constraint Posting method (PCP), one of the most successful approaches for the RCPSP. The proposed approach is considerably simpler compared to existing flow based MCS detection procedures and has better scalability compared to enumeration- and envelope-based ones, while still providing good quality Critical Sets. The method is suitable for problem variants with generalized precedence relations or uncertain/variable durations
Boosting Combinatorial Problem Modeling with Machine Learning
In the past few years, the area of Machine Learning (ML) has witnessed
tremendous advancements, becoming a pervasive technology in a wide range of
applications. One area that can significantly benefit from the use of ML is
Combinatorial Optimization. The three pillars of constraint satisfaction and
optimization problem solving, i.e., modeling, search, and optimization, can
exploit ML techniques to boost their accuracy, efficiency and effectiveness. In
this survey we focus on the modeling component, whose effectiveness is crucial
for solving the problem. The modeling activity has been traditionally shaped by
optimization and domain experts, interacting to provide realistic results.
Machine Learning techniques can tremendously ease the process, and exploit the
available data to either create models or refine expert-designed ones. In this
survey we cover approaches that have been recently proposed to enhance the
modeling process by learning either single constraints, objective functions, or
the whole model. We highlight common themes to multiple approaches and draw
connections with related fields of research.Comment: Originally submitted to IJCAI201
A CHR-based Implementation of Known Arc-Consistency
In classical CLP(FD) systems, domains of variables are completely known at
the beginning of the constraint propagation process. However, in systems
interacting with an external environment, acquiring the whole domains of
variables before the beginning of constraint propagation may cause waste of
computation time, or even obsolescence of the acquired data at the time of use.
For such cases, the Interactive Constraint Satisfaction Problem (ICSP) model
has been proposed as an extension of the CSP model, to make it possible to
start constraint propagation even when domains are not fully known, performing
acquisition of domain elements only when necessary, and without the need for
restarting the propagation after every acquisition.
In this paper, we show how a solver for the two sorted CLP language, defined
in previous work, to express ICSPs, has been implemented in the Constraint
Handling Rules (CHR) language, a declarative language particularly suitable for
high level implementation of constraint solvers.Comment: 22 pages, 2 figures, 1 table To appear in Theory and Practice of
Logic Programming (TPLP
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the
big scale of the systems and the high number of components. The current state
of the art for automated anomaly detection employs Machine Learning methods or
statistical regression models in a supervised fashion, meaning that the
detection tool is trained to distinguish among a fixed set of behaviour classes
(healthy and unhealthy states).
We propose a novel approach for anomaly detection in High Performance
Computing systems based on a Machine (Deep) Learning technique, namely a type
of neural network called autoencoder. The key idea is to train a set of
autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes
and, after training, use them to identify abnormal conditions. This is
different from previous approaches which where based on learning the abnormal
condition, for which there are much smaller datasets (since it is very hard to
identify them to begin with).
We test our approach on a real supercomputer equipped with a fine-grained,
scalable monitoring infrastructure that can provide large amount of data to
characterize the system behaviour. The results are extremely promising: after
the training phase to learn the normal system behaviour, our method is capable
of detecting anomalies that have never been seen before with a very good
accuracy (values ranging between 88% and 96%).Comment: 9 pages, 3 figure
Multi-Criteria Optimal Planning for Energy Policies in CLP
In the policy making process a number of disparate and diverse issues such as
economic development, environmental aspects, as well as the social acceptance
of the policy, need to be considered. A single person might not have all the
required expertises, and decision support systems featuring optimization
components can help to assess policies. Leveraging on previous work on
Strategic Environmental Assessment, we developed a fully-fledged system that is
able to provide optimal plans with respect to a given objective, to perform
multi-objective optimization and provide sets of Pareto optimal plans, and to
visually compare them. Each plan is environmentally assessed and its footprint
is evaluated. The heart of the system is an application developed in a popular
Constraint Logic Programming system on the Reals sort. It has been equipped
with a web service module that can be queried through standard interfaces, and
an intuitive graphic user interface.Comment: Accepted at ICLP2014 Conference as Technical Communication, due to
appear in Theory and Practice of Logic Programming (TPLP
Teaching the Old Dog New Tricks: Supervised Learning with Constraints
Adding constraint support in Machine Learning has the potential to address
outstanding issues in data-driven AI systems, such as safety and fairness.
Existing approaches typically apply constrained optimization techniques to ML
training, enforce constraint satisfaction by adjusting the model design, or use
constraints to correct the output. Here, we investigate a different,
complementary, strategy based on "teaching" constraint satisfaction to a
supervised ML method via the direct use of a state-of-the-art constraint
solver: this enables taking advantage of decades of research on constrained
optimization with limited effort. In practice, we use a decomposition scheme
alternating master steps (in charge of enforcing the constraints) and learner
steps (where any supervised ML model and training algorithm can be employed).
The process leads to approximate constraint satisfaction in general, and
convergence properties are difficult to establish; despite this fact, we found
empirically that even a na\"ive setup of our approach performs well on ML tasks
with fairness constraints, and on classical datasets with synthetic
constraints
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