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Many machine learning tasks have a measure of success that is naturally continuous, such as error under a loss function. We generalize the Algorithmic Search Framework (ASF), used for modeling machine learning domains as discrete search problems, to the continuous space. Moving from discrete target sets to a continuous measure of success extends the applicability of the ASF by allowing us to model fundamentally continuous notions like fuzzy membership. We generalize many results from the discrete ASF to the continuous space and prove novel results for a continuous measure of success. Additionally, we derive an upper bound for the expected performance of a search algorithm under arbitrary levels of quantization in the success measure, demonstrating a negative relationship between quantization and the performance upper bound. These results improve the fidelity of the ASF as a framework for modeling a range of machine learning and artificial intelligence tasks. 

With the rapid growth of large language models, big data, and malicious online attacks, it has become increasingly important to have tools for anomaly detection that can distinguish machine from human, fair from unfair, and dangerous from safe. Prior work has shown that two-distribution (specified complexity) hypothesis tests are useful tools for such tasks, aiding in detecting bias in datasets and providing artificial agents with the ability to recognize artifacts that are likely to have been designed by humans and pose a threat. However, existing work on two-distribution hypothesis tests requires exact values for the specification function, which can often be costly or impossible to compute. In this work, we prove novel finite-sample bounds that allow for two-distribution hypothesis tests with only estimates of required quantities, such as specification function values. Significantly, the resulting bounds do not require knowledge of the true distribution, distinguishing them from traditional p-values. We apply our bounds to detect student cheating on multiple-choice tests, as an example where the exact specification function is unknown. We additionally apply our results to detect representational bias in machine-learning datasets and provide artificial agents with intention perception, showing that our results are consistent with prior work despite only requiring a finite sample of the space. Finally, we discuss additional applications and provide guidance for those applying these bounds to their own work. 

We present an abductive search strategy that integrates creative abduction and probabilistic reasoning to produce plausible explanations for unexplained observations. Using a graphical model representation of abductive search, we introduce a heuristic approach to hypothesis generation, comparison, and selection. To identify creative and plausible explanations, we propose 1) applying novel structural similarity metrics to a search for simple explanations, and 2) optimizing for the probability of a hypothesis’ occurrence given known observations. 

Being able to assess and calculate risks can positively impact an agent’s chances of survival. When other intelligent agents alter environments to create traps, the ability to detect such intended traps (and avoid them) could be life-saving. We investigate whether there are cases for which an agent’s ability to perceive intention through the assessment of environmental artifacts provides a measurable survival advantage. Our agents are virtual gophers assessing a series of room-like environments, which are potentially dangerous traps intended to harm them. Using statistical hypothesis tests based on configuration coherence, the gophers differentiate between designed traps and configurations that are randomly generated and most likely safe, allowing them access to the food contained within them. We find that gophers possessing the ability to perceive intention have significantly better survival outcomes than those without intention perception in most of the cases evaluated.