scholarly journals Deterministic Oversubscription Planning as Heuristic Search: Abstractions and Reformulations

2015 ◽  
Vol 52 ◽  
pp. 97-169 ◽  
Author(s):  
Carmel Domshlak ◽  
Vitaly Mirkis
Keyword(s):  
Heuristic Search ◽  
Lower Cost ◽  
Empirical Evaluation ◽  
Search Space ◽  
Optimal Planning ◽  
Approximation Techniques ◽  
Total Action ◽  
Effective Domain ◽  
Domain Independent ◽  
Made In

While in classical planning the objective is to achieve one of the equally attractive goal states at as low total action cost as possible, the objective in deterministic oversubscription planning (OSP) is to achieve an as valuable as possible subset of goals within a fixed allowance of the total action cost. Although numerous applications in various fields share the latter objective, no substantial algorithmic advances have been made in deterministic OSP. Tracing the key sources of progress in classical planning, we identify a severe lack of effective domain-independent approximations for OSP. With our focus here on optimal planning, our goal is to bridge this gap. Two classes of approximation techniques have been found especially useful in the context of optimal classical planning: those based on state-space abstractions and those based on logical landmarks for goal reachability. The question we study here is whether some similar-in-spirit, yet possibly mathematically different, approximation techniques can be developed for OSP. In the context of abstractions, we define the notion of additive abstractions for OSP, study the complexity of deriving effective abstractions from a rich space of hypotheses, and reveal some substantial, empirically relevant islands of tractability. In the context of landmarks, we show how standard goal-reachability landmarks of certain classical planning tasks can be compiled into the OSP task of interest, resulting in an equivalent OSP task with a lower cost allowance, and thus with a smaller search space. Our empirical evaluation confirms the effectiveness of the proposed techniques, and opens a wide gate for further developments in oversubscription planning.

STATE-SPACE PLANNING WITH VARIANTS OF A*

2006 ◽  
Vol 15 (03) ◽  
pp. 433-464 ◽  
Author(s):  
AMOL DATTATRAYA MALI ◽  
MINH TANG
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Empirical Evaluation ◽  
Heuristic Evaluation ◽  
Lazy Evaluation ◽  
Two Phase ◽  
Space Planning ◽  
Future Work ◽  
Phase Evaluation ◽  
Domain Independent

Significant advances have occurred in heuristic search for planning in the last eleven years. Many of these planners use A*-style search. We report on five sound and complete domain-independent forward state-space STRIPS planners in this paper. The planners are AWA* (Adjusted Weighted A*), MAWA* (Modified AWA*), AWA*-AC (AWA* with action conflict-based adjustment), AWA*-PD (AWA* with deleted preconditions-based adjustment), and AWA*-AC-LE (AWA*-AC with lazy evaluation). AWA* is the first planner to use node-dependent weighting in A*. MAWA*, AWA*-AC, AWA*-PD, and AWA*-AC-LE use conditional two-phase heuristic evaluation. MAWA* applies node-dependent weighting to a subset of the nodes in the fringe, after the two-phase evaluation. One novel idea in AWA*-AC-LE is lazy heuristic evaluation which does not construct relaxed plans to compute heuristic values for all nodes. We report on an empirical comparison of AWA*, MAWA*, AWA*-AC, AWA*-PD, and AWA*-AC-LE with classical planners AltAlt, FF, HSP-2 and STAN 4. Our variants of A* outperform these planners on several problems. The empirical evaluation shows that heuristic search planning is significantly benefitted by node-dependent weighting, conditional two-phase heuristic evaluation and lazy evaluation. We report on the insights about inferior performance of our planners in some domains using the notion of waiting time. We discuss many other variants of A*, state-space planners and directions for future work.

scholarly journals Implicit Abstraction Heuristics

2010 ◽  
Vol 39 ◽  
pp. 51-126 ◽  
Author(s):  
M. Katz ◽  
C. Domshlak
Keyword(s):  
Heuristic Search ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
The State ◽  
Optimal Planning ◽  
Heuristic Function ◽  
Admissible Heuristics ◽  
State Space Search ◽  
Abstract Spaces ◽  
Exponential Size

State-space search with explicit abstraction heuristics is at the state of the art of cost-optimal planning. These heuristics are inherently limited, nonetheless, because the size of the abstract space must be bounded by some, even if a very large, constant. Targeting this shortcoming, we introduce the notion of (additive) implicit abstractions, in which the planning task is abstracted by instances of tractable fragments of optimal planning. We then introduce a concrete setting of this framework, called fork-decomposition, that is based on two novel fragments of tractable cost-optimal planning. The induced admissible heuristics are then studied formally and empirically. This study testifies for the accuracy of the fork decomposition heuristics, yet our empirical evaluation also stresses the tradeoff between their accuracy and the runtime complexity of computing them. Indeed, some of the power of the explicit abstraction heuristics comes from precomputing the heuristic function offline and then determining h(s) for each evaluated state s by a very fast lookup in a ``database.'' By contrast, while fork-decomposition heuristics can be calculated in polynomial time, computing them is far from being fast. To address this problem, we show that the time-per-node complexity bottleneck of the fork-decomposition heuristics can be successfully overcome. We demonstrate that an equivalent of the explicit abstraction notion of a ``database'' exists for the fork-decomposition abstractions as well, despite their exponential-size abstract spaces. We then verify empirically that heuristic search with the ``databased" fork-decomposition heuristics favorably competes with the state of the art of cost-optimal planning.

scholarly journals Very Fast and Accurate Procedure for the Characterization of Photovoltaic Panels from Datasheet Information

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Antonino Laudani ◽  
Francesco Riganti Fulginei ◽  
Alessandro Salvini ◽  
Gabriele Maria Lozito ◽  
Salvatore Coco
Keyword(s):  
Numerical Methods ◽  
Search Space ◽  
Extraction Process ◽  
Reduced Form ◽  
Photovoltaic Panels ◽  
Approximation Techniques ◽  
Computational Costs ◽  
Minimization Technique ◽  
Accurate Procedure

In recent years several numerical methods have been proposed to identify the five-parameter model of photovoltaic panels from manufacturer datasheets also by introducing simplification or approximation techniques. In this paper we present a fast and accurate procedure for obtaining the parameters of the five-parameter model by starting from its reduced form. The procedure allows characterizing, in few seconds, thousands of photovoltaic panels present on the standard databases. It introduces and takes advantage of further important mathematical considerations without any model simplifications or data approximations. In particular the five parameters are divided in two groups, independent and dependent parameters, in order to reduce the dimensions of the search space. The partitioning of the parameters provides a strong advantage in terms of convergence, computational costs, and execution time of the present approach. Validations on thousands of photovoltaic panels are presented that show how it is possible to make easy and efficient the extraction process of the five parameters, without taking care of choosing a specific solver algorithm but simply by using any deterministic optimization/minimization technique.

Heuristics for Domain-Independent Planning: A Survey

2011 ◽  
Vol 135-136 ◽  
pp. 573-577 ◽  
Author(s):  
Rui Shi Liang ◽  
Min Huang
Keyword(s):  
State Space ◽  
Heuristic Search ◽  
Research Work ◽  
Future Research ◽  
Survey Analysis ◽  
Work Related ◽  
State Space Search ◽  
Survey Results ◽  
Domain Independent ◽  
Intense Research

Increasing interest has been devoted to Planning as Heuristic Search over the years. Intense research has focused on deriving fast and accurate heuristics for domain-independent planning. This paper reports on an extensive survey and analysis of research work related to heuristic derivation techniques for state space search. Survey results reveal that heuristic techniques have been extensively applied in many efficient planners and result in impressive performances. We extend the survey analysis to suggest promising avenues for future research in heuristic derivation and heuristic search techniques.

scholarly journals Scaling Up AND/OR Abstraction Sampling

2020 ◽  
Author(s):  
Kalev Kask ◽  
Bobak Pezeshki ◽  
Filjor Broka ◽  
Alexander Ihler ◽  
Rina Dechter
Keyword(s):  
Importance Sampling ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Search Space ◽  
Scaling Up ◽  
Sampling Scheme ◽  
Search Spaces

Abstraction Sampling (AS) is a recently introduced enhancement of Importance Sampling that exploits stratification by using a notion of abstractions: groupings of similar nodes into abstract states. It was previously shown that AS performs particularly well when sampling over an AND/OR search space; however, existing schemes were limited to ``proper'' abstractions in order to ensure unbiasedness, severely hindering scalability. In this paper, we introduce AOAS, a new Abstraction Sampling scheme on AND/OR search spaces that allow more flexible use of abstractions by circumventing the properness requirement. We analyze the properties of this new algorithm and, in an extensive empirical evaluation on five benchmarks, over 480 problems, and comparing against other state of the art algorithms, illustrate AOAS's properties and show that it provides a far more powerful and competitive Abstraction Sampling framework.

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

2021 ◽  
pp. 2250012
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Energy Curve ◽  
Local Optimum ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Heuristic Search Methods ◽  
Multi Level ◽  
Computationally Expensive

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

Heuristic Search Space Generation for Maximum Clique Problem Inspired in Biomolecular Filtering

2015 ◽  
Vol 83 (3) ◽  
pp. 389-400 ◽  
Author(s):  
Nelson E. Ordóñez-Guillén ◽  
Israel M. Martínez-Pérez
Keyword(s):  
Heuristic Search ◽  
Maximum Clique ◽  
Search Space ◽  
Maximum Clique Problem ◽  
Clique Problem

scholarly journals Graph Abstraction in Real-time Heuristic Search

2007 ◽  
Vol 30 ◽  
pp. 51-100 ◽  
Author(s):  
V. Bulitko ◽  
N. Sturtevant ◽  
J. Lu ◽  
T. Yau
Keyword(s):  
Real Time ◽  
Heuristic Search ◽  
Search Space ◽  
Problem Size ◽  
Heuristic Function ◽  
Planning Time ◽  
Learning Speed ◽  
Heuristic Search Methods ◽  
State Abstraction ◽  
The Impact

Real-time heuristic search methods are used by situated agents in applications that require the amount of planning per move to be independent of the problem size. Such agents plan only a few actions at a time in a local search space and avoid getting trapped in local minima by improving their heuristic function over time. We extend a wide class of real-time search algorithms with automatically-built state abstraction and prove completeness and convergence of the resulting family of algorithms. We then analyze the impact of abstraction in an extensive empirical study in real-time pathfinding. Abstraction is found to improve efficiency by providing better trading offs between planning time, learning speed and other negatively correlated performance measures.

scholarly journals Frontier Search and Plan Reconstruction in Oversubscription Planning

2019 ◽  
Vol 33 ◽  
pp. 9999-10000
Author(s):  
Daniel Muller
Keyword(s):  
Recent Work ◽  
Heuristic Search ◽  
Reference Point ◽  
Search Algorithm ◽  
Utility Functions ◽  
Action Sequence ◽  
New Information ◽  
New Knowledge ◽  
Total Action ◽  
High Utility

Oversubscription planning (OSP) is the problem of choosing an action sequence which reaches a state with a high utility, given a budget for total action cost. This formulation allows us to handle situations with under-constrained resources, which do not allow us to achieve all possible goal facts. In optimal OSP, the task is further constrained to finding a path which achieves a state with maximal utility. An incremental BFBB search algorithm with landmark-based approximations, proposed for OSP heuristic search to address tasks with non-negative and 0-binary utility functions. Incremental BFBB maintained with the best solution so far and a set of reference states, extended with all the non-redundant value-carrying states discovered during the search. Each iteration requires search re-start in order to exploit the new knowledge obtained along the search. Recent work proposed an approach of relative estimation of achievements with value-driven landmarks to address arbitrary utility functions, which incrementally improves the best existing solution so far eliminating the need to maintain a set of reference states. We now propose a progressive frontier search algorithm, which alleviates the need to re-start from scratch once new information is acquired by capturing the frontier achieved at the end of each iteration which is used as a dynamic reference point to continue the search, leading to improved efficiency of the search.

scholarly journals The Complexity of Optimal Monotonic Planning: The Bad, The Good, and The Causal Graph

2013 ◽  
Vol 48 ◽  
pp. 783-812 ◽  
Author(s):  
C. Domshlak ◽  
A. Nazarenko
Keyword(s):  
Polynomial Time ◽  
State Of The Art ◽  
Optimal Planning ◽  
Graph Topology ◽  
Causal Graph ◽  
Time Optimal ◽  
Domain Independent ◽  
Positive Results

For almost two decades, monotonic, or ``delete free,'' relaxation has been one of the key auxiliary tools in the practice of domain-independent deterministic planning. In the particular contexts of both satisficing and optimal planning, it underlies most state-of-the-art heuristic functions. While satisficing planning for monotonic tasks is polynomial-time, optimal planning for monotonic tasks is NP-equivalent. Here we establish both negative and positive results on the complexity of some wide fragments of optimal monotonic planning, with the fragments being defined around the causal graph topology. Our results shed some light on the link between the complexity of general optimal planning and the complexity of optimal planning for the respective monotonic relaxations.

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