On the Practical use of Variable Elimination in Constraint Optimization Problems: 'Still-life' as a Case Study

Variable elimination is a general technique for constraint processing. It is often discarded because of its high space complexity. However, it can be extremely useful when combined with other techniques. In this paper we study the applicability of variable elimination to the challenging problem of finding still-lifes. We illustrate several alternatives: variable elimination as a stand-alone algorithm, interleaved with search, and as a source of good quality lower bounds. We show that these techniques are the best known option both theoretically and empirically. In our experiments we have been able to solve the n=20 instance, which is far beyond reach with alternative approaches

A New Oceanographic Data Portal: Padjadjaran Oceanographic Data Centre (PODC)

Understanding the physio-chemical oceanic and atmospheric processes is critical in monitoring climate change. Archipelagic and Small Island countries are vulnerable to the detrimental effects of climate change, and open access oceanic databases can solve data limitations leading to further development of action plans and government policies. A website was developed (www.isea-podc.org) to distribute and augment free oceanographic data based on various in-situ sampling instruments. Oceanographers review the data collected and stored in the portal. It is led by the Marine Research Laboratory (MEAL), Padjadjaran University, in partnership with Marine Science Institute (MSI), University of the Philippines. This framework supplements information that can support marine ecosystems, fisheries, and climate science studies. Furthermore, all data are accessible to not only the academe but also decision-makers in all aspects. The data sources are student research and the new instruments (RHEA and ARHEA) developed by MEAL. In the future, the portal will be integrated with other government institutional data to provide other functional features and can yield network-wide analyses. In the next phase, collaboration from ASEAN countries should be conducted to gain more impact and provide robust datasets

Optimization of Single-Satellite Operational Schedules Towards Enhanced Communication Capacity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97112/1/AIAA2012-4610.pd

An AI planning-based tool for scheduling satellite nominal operations

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.Publicad

Best-first and/or search for graphical models

Abstract The paper presents and evaluates the power of best-first search over AND/OR search spaces in graphical models. The main virtue of the AND/OR representation is its sensitivity to the structure of the graphical model, which can translate into significant time savings. Indeed, in recent years depth-first AND/OR Branch-and-Bound algorithms were shown to be very effective when exploring such search spaces, especially when using caching. Since best-first strategies are known to be superior to depth-first when memory is utilized, exploring the best-first control strategy is called for. In this paper we introduce two classes of best-first AND/OR search algorithms: those that explore a context-minimal AND/OR search graph and use static variable orderings, and those that use dynamic variable orderings but explore an AND/OR search tree. The superiority of the best-first search approach is demonstrated empirically on various real-world benchmarks

An Exact Algorithm for Optimal Areal Positioning Problem with Rectangular Targets and Requests

In this thesis, we introduce a new class of problems, which we call Optimal Areal Positioning (OAP), and study a special form of these problems. OAPs have important applications in earth observation satellite management, tele-robotics, multi-camera control, and surveillance. In OAP, we would like to find the optimal position of a set of floating geometric objects (targets) on a two-dimensional plane to (partially) cover another set of fixed geometric objects (requests) in order to maximize the total reward obtained from covered parts of requests. In this thesis, we consider the special form of OAP in which targets and requests are parallel axes rectangles and targets are of equal size. A predetermined reward is associated with covering an area unit of each request. Based on the number of target rectangles, we classify rectangular OAP into two categories: Single Target Problem (STP) and Multi-Target Problem (MTP). The structure of MTP can be compared to the planar p-center which is NP-complete, if p is part of the input. In fact, we conjecture that MTP is NP-complete. The existing literature does not contain any work on MTP. The research contributions of this thesis are as follows: We develop new theoretical properties for the solution of STP and devised a new solution approach for it. This approach is based on a novel branch-and-bound (BB) algorithm devised over a reduced solution space. Branching is done using a clustering scheme. Our computational results show that in many cases our approach significantly outperforms the existing Plateau Vertex Traversal and brute force algorithms, especially for problems with many requests appearing in clusters over a large region. We perform a theoretical study of MTP for the first time and prove several theoretical properties for its solution. We have introduced a reduced solution space using these properties. We present the first exact algorithm to solve MTP. This algorithm has a branch-and-bound framework. The reduced solution space calls for a novel branching strategy for MTP. The algorithm has a main branch-and-bound tree with a special structure along with two trees (one for each axis) to store the information required for branching in the main tree in an efficient format. Branching is done using a clustering scheme. We perform computational experiments to evaluate the performance of our algorithm. Our algorithm solves relatively large instances of MTP in a short time

Residual-guided look-ahead in AND/OR search for graphical models

We introduce the concept of local bucket error for the mini-bucket heuristics and show how it can be used to improve the power of AND/OR search for combinatorial optimization tasks in graphical models (e.g. MAP/MPE or weighted CSPs). The local bucket error illuminates how the heuristic errors are distributed in the search space, guided by the mini-bucket heuristic. We present and analyze methods for compiling the local bucket-errors (exactly and approximately) and show that they can be used to yield an effective tool for balancing look-ahead overhead during search. This can be especially instrumental when memory is restricted, accommodating the generation of only weak compiled heuristics. We illustrate the impact of the proposed schemes in an extensive empirical evaluation for both finding exact solutions and anytime suboptimal solutions.Peer ReviewedPostprint (published version

Constrained Optimal Orbit Design for Earth Observation

The purpose of this dissertation is to demonstrate use requirements for a satellite observation mission can be used to determine a constrained optimal orbit based on observation site requirements, observation condition restraints, and sensor characteristics. The typical Earth observation satellite is first designed according to an appropriate orbit; then the observation requirements are used to develop a target schedule. The new design process outlines the development of the appropriate orbit by incorporating user requirements at the forefront of mission planning, not after an orbit has been selected. This research shows how to map the user requirements into constraints for the cost function and optimization process. A global case study with variations demonstrates the effectiveness of the design process. Additionally, a case study is performed for a regional or clustered set of targets. Finally, a lifecycle analysis tests the orbit in a full perturbation environment to evaluate the changes in the ideal orbital elements over time without orbit maintenance or corrections