Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

In classical AI planning, heuristic functions typically base their estimates on a relaxation of the input task. Such relaxations can be more or less precise, and many heuristic functions have a refinement procedure that can be iteratively applied until the desired degree of precision is reached. Traditionally, such refinement is performed offline to instantiate the heuristic for the search. However, a natural idea is to perform such refinement online instead, in situations where the heuristic is not sufficiently accurate. We introduce several online-refinement search algorithms, based on hill-climbing and greedy best-first search. Our hill-climbing algorithms perform a bounded lookahead, proceeding to a state with lower heuristic value than the root state of the lookahead if such a state exists, or refining the heuristic otherwise to remove such a local minimum from the search space surface. These algorithms are complete if the refinement procedure satisfies a suitable convergence property. We transfer the idea of bounded lookaheads to greedy best-first search with a lightweight lookahead after each expansion, serving both as a method to boost search progress and to detect when the heuristic is inaccurate, identifying an opportunity for online refinement. We evaluate our algorithms with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness. On both the IPC domains as well as on the recently published Autoscale benchmarks, our online-refinement search algorithms significantly beat state-of-the-art satisficing planners, and are competitive even with complex portfolios.

Analisis Metode Quantum untuk Optimalisasi Algoritma 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.

Type-WA*: Using Exploration in Bounded Suboptimal Planning

Previous work on satisficing planning using greedy best-first search (GBFS) has shown that non-greedy, randomized exploration can help escape uninformative heuristic regions and solve hard problems faster. Despite their success when used with GBFS, such exploration techniques cannot be directly applied to bounded suboptimal algorithms like Weighted A* (WA*) without losing the solution-quality guarantees. In this work, we present Type-WA*, a novel bounded suboptimal planning algorithm that augments WA* with type-based exploration while still satisfying WA*'s theoretical solution-quality guarantee. Our empirical analysis shows that Type-WA* significantly increases the number of solved problems, when used in conjunction with each of three popular heuristics. Our analysis also provides insight into the runtime vs. solution cost trade-off.

Finding Optimal Algorithm in Artificial Intelligence

Present days many artificial intelligence search algorithms are plays a important to figure out the problem of shortest path finding. The paper presents the detailed study of heuristic search and blind search techniques. The paper focus additional in the direction of blind search strategies such as Breadth First Search, Depth First Search, and Uniform Cost Search and informed explore strategies like A*, and Best First Search. The paper consist of effective of search procedure, their qualities, and demerits, where these algorithms are applicable, also at last comparison of search techniques based on complexity, optimality and completeness are presented in tabular structure.

Efficient Lifted Planning with Regression-Based Heuristics

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.

Efficient Lifted Planning with Regression-Based Heuristics

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.

Remainder Imunisasi Pada Sistem Informasi Posyandu untuk Memantau Kelengkapan Imunisasi Rutin Anak di Masa Pandemi Covid-19

Pandemi Covid-19 membuat banyak orang tua telat bahkan tidak memberikan imunisasi rutin kepada anaknya karena takut akan terpapar virus jika mendatangi tempat pemberian imunisasi. Sesuai surat edaran Kemenkes 24 Maret 2020, pemerintah mewajibkan agar seluruh pelayanan kesehatan seperti Puskesmas dan Posyandu melaksanakan kegiatan pemberian imunisai sesuai dengan ketentuan dan proteksi kesehatan.Untuk membantu bidan dan kader posyandu dalam memantau imunisasi rutin anak,dibuatkanlah remainder imunisasi pada Sistem Informasi Posyandu. Metode Forward Chainning digunakan sebagai pelacak imunisasi dan Best First Search sebagai pencari penentuan keputusan untuk memunculkan remainder imunisasi secara otomatis. Rule imunisasi yang digunakan untuk pelacakan sesuai dengan aturan imunisasi rutin anak yang dikeluarkan oleh IDAI dan konsultasi dengan bidan sebagai pakar. Tanggal lahir anak digunakan sebagai acuan pelacakan sesuai dengan rule imunisasi rutin anak untuk memunculkan remainder imunisasi anak. Ujicoba yang dilakukan pada Sistem menggunakan blackbox testing dengan hasil semua fitur berfungsi dengan baik. Remainder imunisasi anak dapat membantu bidan dan kader untuk memantau imunisasi rutin anak di Posyandu khususnya saat pandemi Covid-19. Abstract The Covid-19 pandemic has made many parents late even not giving routine immunizations to their children for fear of being exposed to the virus if they come to the immunization site. According to the Ministry of Health circular letter March 24, 2020, the government requires that all health services such as Puskesmas and Posyandu carry out immunization activities in accordance with health provisions and protection. To assist midwives and posyandu cadres in monitoring routine child immunizations, immunization remainder are made in the Posyandu Information System. The Forward Chainning method is used as an immunization tracker and the Best First Search as a decision-making search to bring up the immunization remainder automatically. The immunization rules used for tracking are in accordance with the routine immunization rules for children issued by IDAI and consultation with midwives as experts. The child's date of birth is used as a tracking reference according to the routine child immunization rule to bring up the child immunization remainder. Tests carried out on the system use blackbox testing with the results of all features functioning properly. Child immunization remainder can help midwives and cadres to monitor routine immunizations for children at Posyandu, especially during the Covid-19 pandemic.