The paper considers Weighted Best First (WBF) search schemes, popular for path-finding domain, as approxi-mations and as anytime schemes for the MAP task. We demonstrate empirically the ability of these schemes to effectively provide approximations with guaranteed suboptimality and also show that as anytime schemes they can be competitive on some benchmarks with one of the best state-of-the-art scheme, Depth-First Branch-and-Bound
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Tree search is a common technique for solving constraint satisfaction and combinatorial optimization...
We introduce new anytime search algorithms that combine best-first with depth-first search into hybr...
This paper explores the anytime performance of search-based algorithms for solving the Marginal MAP ...
Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be very difficu...
Marginal MAP is known to be a difficult task for graphical models, particularly because the evalu-at...
Most practitioners use a variant of the Alpha-Beta algorithm, a simple depth-first procedure, for se...
We present an algorithm that exploits the complimentary benefits of best-first search (BFS) and dept...
Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art sol...
There are two major paradigms for linear-space heuristic search: iterative deepening (IDA*) and recu...
Best-first search (BFS) expands the fewest nodes among all admissible algorithms us-ing the same cos...
Abstract. The critical resource that limits the application of best-first search is memory. We prese...
While suboptimal best-first search algorithms like Greedy Best-First Search are frequently used when...
Heuristic functions play an important role in drastically improving performance of satisficing plann...
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Tree search is a common technique for solving constraint satisfaction and combinatorial optimization...
We introduce new anytime search algorithms that combine best-first with depth-first search into hybr...
This paper explores the anytime performance of search-based algorithms for solving the Marginal MAP ...
Marginal MAP is a key task in Bayesian inference and decision-making. It is known to be very difficu...
Marginal MAP is known to be a difficult task for graphical models, particularly because the evalu-at...
Most practitioners use a variant of the Alpha-Beta algorithm, a simple depth-first procedure, for se...
We present an algorithm that exploits the complimentary benefits of best-first search (BFS) and dept...
Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art sol...
There are two major paradigms for linear-space heuristic search: iterative deepening (IDA*) and recu...
Best-first search (BFS) expands the fewest nodes among all admissible algorithms us-ing the same cos...
Abstract. The critical resource that limits the application of best-first search is memory. We prese...
While suboptimal best-first search algorithms like Greedy Best-First Search are frequently used when...
Heuristic functions play an important role in drastically improving performance of satisficing plann...
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Greedy Best-First Search (GBFS) is a powerful algorithm at the heart of many state of the art satisf...
Tree search is a common technique for solving constraint satisfaction and combinatorial optimization...