In the field of Sequential Decision Making (SDM), two paradigms have historically vied for
supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

This paper presents FAMA, a novel approach for learning Strips action models from
observations of plan executions that compiles the learning task into a classical planning …

The objective of goal recognition is to infer a goal that accounts for the observed behavior of
an actor. In this work, we introduce and formalize the notion of active goal recognition in …

Goal Recognition is the task of inferring an agent's goal, from a set of hypotheses, given a
model of the environment dynamic, and a sequence of observations of such agent's …

Every model involves assumptions. While some are standard to all models that simulate
intelligent decision-making (eg, discrete/continuous, static/dynamic), goal recognition is well …

Stateless model checking (SMC) is one of the standard approaches to the verification of
concurrent programs. As scheduling non-determinism creates exponentially large spaces of …

Goal recognition is the problem of determining an agent's intent by observing her behaviour.
Contemporary solutions for general task-planning relate the probability of a goal to the cost …

Contemporary cost-based goal-recognition assumes rationality: that observed behaviour is
more or less optimal. Probabilistic goal recognition systems, however, explicitly depend on …

We present a causality-based algorithm for solving two-player reachability games
represented by logical constraints. These games are a useful formalism to model a wide …