A Fuzzy Reasoning Model for Action and Change in Timed Domains

Abstract

This paper proposes a fuzzy approach for reasoning about action and change in timed domains. In our method, actions and world states are modeled as fuzzy sets over time axis. Thus, their temporal relations and time constraints can be modeled as fuzzy rules. So, our method handles well the issue that action happens at an approximate time and then the states also change at an approximate time, which has not been solved well in the existing work. Finally, our method is used to solve the classic problem of rail‐road crossing control in a fuzzy environment. The theoretical and simulation analysis shows that the controller using our method works well.

HKUST-Landslide Susceptibility Dataset (HKUST-LSD): A benchmark dataset for landslide susceptibility assessment in Hong Kong

“…Enabling LMs to understand distributional changes due to localized causal interventions, particularly in semantic spaces, has long been a crucial objective in the pursuit of conscious machine intelligence [7,8]. Previous works have mainly explored this within the context of discriminating changes between actions and states with methods such as commonsense knowledge injection [58], event calculus [5], and fuzzy reasoning [77]. Other studies aim to benchmark this reasoning process through logical reasoning tasks [25] and planning tasks [62,71].…”

A Fuzzy Dynamic Belief Logic System

“…The theory of fuzzy set and logic is a powerful tool for imprecise information and applied in many areas . Here we just discuss two main streams related to our work in this paper: fuzzy modal logic and fuzzy belief revision .…”

“…The theory of fuzzy set and logic is a powerful tool for imprecise information and applied in many areas. 16,17,43,44 Here we just discuss two main streams related to our work in this paper: fuzzy modal logic [45][46][47] and fuzzy belief revision. 43,48 In the field of fuzzy modal logic, the work of Hájek 49 studies the fuzzy variant of the well-known modal logic S5.…”

“…In the logic systems above, the information and believes in formulas are crisp, i.e., either the information is believed or it is not. However, it is not always the case in real life . For instance, in real life we often say someone is a high person.…”

“…However, it is not always the case in real life. [16][17][18] For instance, in real life we often say someone is a high person. Here "high" is a fuzzy conception because there are no crisp divisions between "high" and "low."…”