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Avoiding and Escaping Depressions in Real-Time Heuristic Search

Journal of Artificial Intelligence Research

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA*, easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA* or LRTA*(k), improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions.


Cooperative Search with Concurrent Interactions

Journal of Artificial Intelligence Research

In this paper we show how taking advantage of autonomous agents' capability to maintain parallel interactions with others, and incorporating it into the cooperative economic search model results in a new search strategy which outperforms current strategies in use. As a framework for our analysis we use the electronic marketplace, where buyer agents have the incentive to search cooperatively. The new search technique is quite intuitive, however its analysis and the process of extracting the optimal search strategy are associated with several significant complexities. These difficulties are derived mainly from the unbounded search space and simultaneous dual affects of decisions taken along the search. We provide a comprehensive analysis of the model, highlighting, demonstrating and proving important characteristics of the optimal search strategy. Consequently, we manage to come up with an efficient modular algorithm for extracting the optimal cooperative search strategy for any given environment. A computational based comparative illustration of the system performance using the new search technique versus the traditional methods is given, emphasizing the main differences in the optimal strategy's structure and the advantage of using the proposed model.


Effective Heuristics for Suboptimal Best-First Search

Journal of Artificial Intelligence Research

Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search.


Infographic: Google: The Most Popular Search on Bing

#artificialintelligence

The beleaguered search engine with dreams of world domination never really had a chance against its all-conquering competitor, Google. Adding insult to injury, analysis by SEO experts Ahrefs has revealed that in 2019, the search Bing is most often tasked with is pointing its disloyal users in the direction of'google'. As of July, the search engine dealt with 44.4 million such queries from around the world, far more than the second most frequent - 'youtube' was searched for 33.3 million times. In another blow for Bing's owner Microsoft, the fifth most common search was people looking for help with Windows 10.


7 things you didn’t know Google Search could do until now

USATODAY - Tech Top Stories

Google Search is much more powerful than most people know. Google is way more powerful than most people realize. Regular searches are helpful, but they don't even scratch the surface of Google's abilities. Sometimes, your basic search inquiries may not be enough or you need a tip to get the best results. Fair warning: You can't mention Google without also mentioning tracking.