Artificial intelligence (AI) systems have become an indispensable component of modern technology. However, research on human behavioral responses is lagging behind, i.e., the research into human reliance on AI advice (AI reliance). Current shortcomings in the literature include the unclear influences on AI reliance, lack of external validity, conflicting approaches to measuring reliance, and disregard for a change in reliance over time. Promising avenues for future research include reliance on generative AI output and reliance in multi-user situations. In conclusion, we present a morphological box that serves as a guide for research on AI reliance.

The Disjunctive Temporal Problem (DTP) involves the satisfaction of aset of constraints represented by disjunctive formulas of the form x1 - y1 r1 or x2 - y2 r2 or ... or xk - yk rk. DTP is a general temporal reasoning problem which includes the well-known Temporal Constraint Satisfaction Problem (TCSP) introduced by Dechter, Meiri and Pearl. This paper describes a basic constraint satisfaction algorithm where several aspects of the current literature are integrated, in particular the so-called incremental forward checking. Hence,two new extended solving strategies are proposed and experimentally evaluated. The new proposed strategies are very competitive with the best results available in the current literature. In addition, the analysis of the empirical results suggests future research directions concerning in particular the use of arc-consistency filtering strategies.

Lung cancer is the leading cause of cancer death among men and women worldwide, according to the American Cancer Society. Each year, more people -- about 154,000 -- die of lung cancer than from colon, breast, and prostate cancers combined, and the lifetime risk of developing lung cancer is as high as 1 in 15. Successful patient outcomes depend on early detection -- of the half of new patients diagnosed after lung cancer has spread, only 4 percent will live for five years. Fortunately, advances in artificial intelligence (AI) could make it easier for clinicians to spot signs of tumor growth more accurately than with eyes alone. A paper recently published on the preprint server Arxiv.org