Comparison of the number of nodes explored by cyclic best first search with depth contour and best first search

Recent research suggests that ICES stock definitions for cod, haddock, and whiting of “west of Scotland” and “North Sea”, do not reflect underlying population structures. As population responses to different vital rates and local pressures would be expected to lead to asynchrony in dynamics, we examined trends in local spawning-stock biomass (SSB) among putative subpopulations of the three species. Delineation of subpopulation boundaries around spawning time was made based on genetic, tagging, and otolith microchemistry studies together with density distributions of species based on research vessel survey data. Subpopulation specific indices of SSB were derived using numbers-at-age and maturity observations from the same research vessel data and asynchrony was assessed by fitting a smoother to log SSB for each subpopulation and testing whether the smooths were parallel. Results for cod support the hypothesis of distinct inshore and larger offshore subpopulations and for whiting for northern and southern North Sea subpopulations with a boundary associated with the 50 m depth contour. In haddock, no difference in SSB trends between the North Sea and west of Scotland was found. For cod and whiting, subpopulation SSB trends differed substantially within current stock assessment units, implying reported stock-based SSB time-series have masked underlying subpopulation trends.

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Prostate Cancer Classification Based on Best First Search and Taguchi Feature Selection Method

Image and Video Technology - Lecture Notes in Computer Science ◽

10.1007/978-3-030-34879-3_25 ◽

2019 ◽

pp. 325-336

Author(s):

Md Akizur Rahman ◽

Priyanka Singh ◽

Ravie Chandren Muniyandi ◽

Domingo Mery ◽

Mukesh Prasad

Keyword(s):

Prostate Cancer ◽

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Cancer Classification ◽

Best First Search ◽

Prostate Cancer Classification

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Efficient Lifted Planning with Regression-Based Heuristics

10.32920/ryerson.14647755 ◽

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.

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Learning Large Logic Programs By Going Beyond Entailment

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/287 ◽

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.

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