Annals of Mathematics and Artificial Intelligence最新文献

Forgetting variables from a propositional formula may increase its size. Introducing new variables is a way to shorten it. Both operations can be expressed in terms of common equivalence, a weakened version of equivalence. In turn, common equivalence can be expressed in terms of forgetting. An algorithm for forgetting and checking common equivalence in polynomial space is given for the Horn case; it is polynomial-time for the subclass of single-head formulae. Minimizing after forgetting is polynomial-time if the formula is also acyclic and variables cannot be introduced, NP-hard when they can.

This article introduces a new decision tree algorithm that accounts for time-varying covariates in the decision-making process. Traditional decision tree algorithms assume that the covariates are static and do not change over time, which can lead to inaccurate predictions in dynamic environments. Other existing methods suggest workaround solutions such as the pseudo-subject approach, discussed in the article. The proposed algorithm utilises a different structure and a time-penalised splitting criterion that allows a recursive partitioning of both the covariates space and time. Relevant historical trends are then inherently involved in the construction of a tree, and are visible and interpretable once it is fit. This approach allows for innovative and highly interpretable analysis in settings where the covariates are subject to change over time. The effectiveness of the algorithm is demonstrated through a real-world data application in life insurance. The results presented in this article can be seen as an introduction or proof-of-concept of our time-penalised approach, and the algorithm’s theoretical properties and comparison against existing approaches on datasets from various fields, including healthcare, finance, insurance, environmental monitoring, and data mining in general, will be explored in forthcoming work.

Contests are commonly used as a mechanism for eliciting effort and participation in multi-agent settings. Naturally, and much like with various other mechanisms, the information provided to the agents prior to and throughout the contest fundamentally influences its outcomes. In this paper we study the problem of information providing whenever the contest organizer does not initially hold the information and obtaining it is potentially costly. As the underlying contest mechanism for our model we use the binary contest, where contestants’ strategy is captured by their decision whether or not to participate in the contest in the first place. Here, it is often the case that the contest organizer can proactively obtain and provide contestants information related to their expected performance in the contest. We provide a comprehensive equilibrium analysis of the model, showing that even when such information is costless, it is not necessarily the case that the contest organizer will prefer to obtain and provide it to all agents, let alone when the information is costly.

The calibration problem in machine learning classification tasks arises when a model’s output score does not align with the ground truth observed probability of the target class. There exist several parametric and non-parametric post-processing methods that can help to calibrate an existing classifier. In this work, we focus on binary classification cases where the dataset is imbalanced, meaning that the negative target class significantly outnumbers the positive one. We propose new parametric calibration methods designed to this specific case and a new calibration measure focusing on the primary objective in imbalanced problems: detecting infrequent positive cases. Experiments on several datasets show that, for imbalanced problems, our approaches outperform state-of-the-art methods in many cases.