The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop
AI systems with more human-like reasoning capabilities by combining symbolic reasoning …

Goal recognition is a crucial aspect of understanding the intentions and objectives of agents
by observing some of their actions. The most prominent approaches to goal recognition can …

Goal Recognition is the task of inferring an agent's intentions from a set of observations.
Existing recognition approaches have made considerable advances in domains such as …

Approaches in AI planning for Cyber-Physical Production Systems (CPPS) are mainly
symbolic and depend on comprehensive formalizations of system domains and planning …

It is generally accepted that all cyber attacks cannot be prevented, creating a need for the
ability to detect and respond to cyber attacks. Both connectionist and symbolic AI are …

Explainability is an essential reason limiting the application of neural networks in many vital
fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging …

For an agent in a multi-agent environment, it is often beneficial to be able to predict what
other agents will do next when deciding how to act. Previous work in multi-agent intention …

Neuro-symbolic AI is an effective method for improving the overall performance of AI models
by combining the advantages of neural networks and symbolic learning. However, there are …

Goal Recognition is the task by which an observer aims to discern the goals that correspond
to plans that comply with the perceived behavior of subject agents given as a sequence of …

We present a new approach to goal recognition that involves comparing observed facts with
their expected probabilities. These probabilities depend on a specified goal g and initial …