scholarly journals Analisis Metode Quantum untuk Optimalisasi Algoritma Best First Search

2021 ◽  
Vol 7 (2) ◽  
pp. 300
Author(s):  
Rafiqa Dewi ◽  
Muhammad Ridwan Lubis
Keyword(s):  
Best First Search

Pada penelitian ini, penulis mengusulkan metode quantum untuk menggantikan perhitungan klasik pada algoritma Best First Search, yang bertujuan untuk meningkatkan algoritma BFS dalam mencari solusi terbaik, dengan membuatnya bekerja lebih cepat. Penulis menggantikan setiap informasi yang disimpan dalam bit ke dalam bentuk qubit. Penulis telah melakukan percobaan terhadap 23 solusi dengan 3 qubit. Setelah menerapkan pendekatan qubit ini pada perhitungan BFS klasik maka diperoleh hasil akhir berupa perolehan solusi terbaik dengan percepatan yang signifikan. Dimana perhitungan BFS Klasik melakukan 8 kali perhitungan sementara BFS Quantum melakukannya hanya dengan 1 kali perhitungan saja.

scholarly journals Efficient Lifted Planning with Regression-Based Heuristics

2021 ◽  
Author(s):  
Hadi Qovaizi
Keyword(s):  
Organic Synthesis ◽  
State Of The Art ◽  
Transition System ◽  
Modern State ◽  
Synthetic Approach ◽  
Best First Search ◽  
Planning Task ◽  
International Planning ◽  
Domain Independent ◽  
Novel Algorithm

Modern state-of-the-art planners operate by generating a grounded transition system prior to performing search for a solution to a given planning task. Some tasks involve a significant number of objects or entail managing predicates and action schemas with a significant number of arguments. Hence, this instantiation procedure can exhaust all available memory and therefore prevent a planner from performing search to find a solution. This thesis explores this limitation by presenting a benchmark set of problems based on Organic Chemistry Synthesis that was submitted to the latest International Planning Competition (IPC-2018). This benchmark was constructed to gauge the performance of the competing planners given that instantiation is an issue. Furthermore, a novel algorithm, the Regression-Based Heuristic Planner (RBHP), is developed with the aim of averting this issue. RBHP was inspired by the retro-synthetic approach commonly used to solve organic synthesis problems efficiently. RBHP solves planning tasks by applying domain independent heuristics, computed by regression, and performing best-first search. In contrast to most modern planners, RBHP computes heuristics backwards by applying the goal-directed regression operator. However, the best-first search proceeds forward similar to other planners. The proposed planner is evaluated on a set of planning tasks included in previous International Planning Competitions (IPC) against a subset of the top scoring state-of-the-art planners submitted to the IPC-2018.

scholarly journals Learning Large Logic Programs By Going Beyond Entailment

2020 ◽  
Author(s):  
Andrew Cropper ◽  
Sebastijan Dumančic
Keyword(s):  
Logic Programming ◽  
Loss Function ◽  
Inductive Logic Programming ◽  
State Of The Art ◽  
Inductive Logic ◽  
Program Synthesis ◽  
Loss Functions ◽  
Logic Programs ◽  
Binary Decision ◽  
Best First Search

A major challenge in inductive logic programming (ILP) is learning large programs. We argue that a key limitation of existing systems is that they use entailment to guide the hypothesis search. This approach is limited because entailment is a binary decision: a hypothesis either entails an example or does not, and there is no intermediate position. To address this limitation, we go beyond entailment and use 'example-dependent' loss functions to guide the search, where a hypothesis can partially cover an example. We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs. Our experiments on three diverse program synthesis domains (robot planning, string transformations, and ASCII art), show that Brute can substantially outperform existing ILP systems, both in terms of predictive accuracies and learning times, and can learn programs 20 times larger than state-of-the-art systems.

A Solution to the GHI Problem for Best-First Search

1999 ◽  
pp. 25-49 ◽  
Author(s):  
Dennis M. Breuker ◽  
H. Jaap van den Herik ◽  
Jos W. H. M. Uiterwijk ◽  
L. Victor Allis
Keyword(s):  
Best First Search
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