Author name cluster

Tal Beja

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

AAAI Conference 2013 Conference Paper

TONIC: Target Oriented Network Intelligence Collection for the Social Web

Roni Stern
Liron Samama
Rami Puzis
Tal Beja
Zahy Bnaya
Ariel Felner

In this paper we introduce the Target Oriented Network Intelligence Collection (TONIC) problem, which is the problem of finding profiles in a social network that contain information about a given target via automated crawling. We formalize TONIC as a search problem and a best-first approach is proposed for solving it. Several heuristics are presented to guide this search. These heuristics are based on the topology of the currently known part of the social network. The efficiency of the proposed heuristics and the effect of the graph topology on their performance is experimentally evaluated on the Google+ social network.

PDF Details

IJCAI Conference 2013 Conference Paper

Towards Rational Deployment of Multiple Heuristics in A*

David Tolpin
Tal Beja
Solomon Eyal Shimony
Ariel Felner
Erez Karpas

The obvious way to use several admissible heuristics in A∗ is to take their maximum. In this paper we aim to reduce the time spent on computing heuristics. We discuss Lazy A∗, a variant of A∗ where heuristics are evaluated lazily: only when they are essential to a decision to be made in the A∗ search process. We present a new rational meta-reasoning based scheme, rational lazy A∗, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Both methods are examined theoretically. Empirical evaluation on several domains supports the theoretical results, and shows that lazy A∗ and rational lazy A∗ are state-of-the-art heuristic combination methods.

PDF Details DOI

SoCS Conference 2013 Conference Paper

Towards Rational Deployment of Multiple Heuristics in A* (Extended Abstract)

David Tolpin
Tal Beja
Solomon Eyal Shimony
Ariel Felner
Erez Karpas

In this paper we discuss and experiment with Lazy A*, a variant of A* where heuristics are evaluated lazily and with Rational Lazy A*, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Full version appears in IJCAI-2013.

Details

SoCS Conference 2012 Conference Paper

Partial-Expansion A* with Selective Node Generation

Ariel Felner
Meir Goldenberg
Guni Sharon
Roni Stern
Tal Beja
Nathan R. Sturtevant
Robert C. Holte
Jonathan Schaeffer 0001

A* is often described as being 'optimal, ' in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. We introduce an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* only generates the children with the same f-cost as the parent. State-of-the-art results were obtained for a number of domains. Drawbacks of EPEA* are also discussed. A full version of this paper appears in the proceedings of AAAI-2012

Details

AAAI Conference 2012 Conference Paper

Partial-Expansion A* with Selective Node Generation

Ariel Felner
Meir Goldenberg
Guni Sharon
Roni Stern
Tal Beja
Nathan Sturtevant
Jonathan Schaeffer
Robert Holte

A* is often described as being ‘optimal’, in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) (Yoshizumi, Miura, and Ishida 2000) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. n is re-inserted into the OPEN list, but with the f-cost of the next best child. This paper introduces an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* generates only the children with the same f-cost as the parent. EPEA* is generalized to its iterative-deepening variant, EPE-IDA*. For some domains, these algorithms yield substantial performance improvements. State-of-the-art results were obtained for the pancake puzzle and for some multi-agent pathfinding instances. Drawbacks of EPEA* are also discussed.

PDF Details