Enhancing Evolutionary Conversion Rate Optimization via Multi-Armed Bandit Algorithms

Conversion rate optimization means designing web interfaces such that more visitors perform a desired action (such as register or purchase) on the site. One promising approach, implemented in Sentient Ascend, is to optimize the design using evolutionary algorithms, evaluating each candidate design online with actual visitors. Because such evaluations are costly and noisy, several challenges emerge: How can available visitor traffic be used most efficiently? How can good solutions be identified most reliably? How can a high conversion rate be maintained during optimization? This paper proposes a new technique to address these issues. Traffic is allocated to candidate solutions using a multi-armed bandit algorithm, using more traffic on those evaluations that are most useful. In a best-arm identification mode, the best candidate can be identified reliably at the end of evolution, and in a campaign mode, the overall conversion rate can be optimized throughout the entire evolution process. Multi-armed bandit algorithms thus improve performance and reliability of machine discovery in noisy real-world environments.

THE EMERGENCE OF ARTIFICIAL CONSCIOUSNESS AND ITS IMPORTANCE TO REACH THE TECHNOLOGICAL SINGULARITY

The men admired a way to swim the fish, but today they sail faster than anyone. They'd like flying like the birds, but have been a lot higher. They searched for wisdom, now they have all the knowledge accumulated in the story available in a few clicks. Human evolution is about to meet its peak through the Technological Singularity, which can be understood as the future milestone reached at the moment that a computer program can think like a human, yet with quick access to all information already registered by society. It will not be like a man, but more intelligent than all mankind in history. So we have a big question: will this new entity has consciousness? Through a study of the levels of intelligent agents autonomy and in a timeless dialogue with Alan Turing, René Descartes, Ludwic Wittgenstein, John Searle and Vernor Vinge, we show the possibility of an artificial consciousness and thatthe quest for intentionality, promoted by sophisticated algorithms of learning and machine discovery, is the key to reach of Technological Singularity.

Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.