Online Goal Recognition as Reasoning over Landmarks

Proceedings of the AAAI Conference on Artificial Intelligence

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.

2016

Recognition of goals and plans using incomplete evidence from action execution can be done e ciently by using automated planning techniques. In many applications it is important to recognize goals and plans not only accurately, but also quickly. In order to address this challenge, we develop recognition approaches based on planning techniques that rely on planning landmarks to filter candidate goals and plans from observations. In automated planning, landmarks are properties or actions that cannot be avoided to achieve a goal. We address the task of recognizing goals and plans without pre-defined static plan libraries, and instead we use a planning domain definition to represent the problem and the expected agent behavior. In this work, we show the applicability of planning techniques for recognition tasks in three settings: first, we use planning landmarks to develop a heuristic-based plan recognition approach; second, we refine an existing planningbased plan recognition approach; ...

2016

Recognition of goals and plans using incomplete evidence from action execution can be done efficiently by using planning techniques. In many applications it is important to recognize goals and plans not only accurately, but also quickly. In this paper, we develop a heuristic approach for recognizing plans based on planning techniques that rely on ordering constraints to filter candidate goals from observations. These ordering constraints are called landmarks in the planning literature, which are facts or actions that cannot be avoided to achieve a goal. We show the applicability of planning landmarks in two settings: first, we use it directly to develop a heuristic-based plan recognition approach; second, we refine an existing planning-based plan recognition approach by pre-filtering its candidate goals. Our empirical evaluation shows that our approach is not only substantially more accurate than the state-of-the-art in all available datasets, it is also an order of magnitude faster.

Synthesis Lectures on Artificial Intelligence and Machine Learning, 2021

Plan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This s ynergistic a rea o f r esearch c ombines, u nites, a nd m akes u se o f t echniques a nd research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.

2020

Goal Recognition is the task of recognizing the intended goal of autonomous agents or humans by observing their behavior in an environment. Over the past years, most existing approaches to goal and plan recognition have been ignoring the need to deal with imperfections regarding the domain model that formalizes the environment where autonomous agents behave. In this work, we introduce the problem of goal recognition over imperfect domain models, and develop solution approaches that explicitly deal with two distinct types of imperfect domains models: (1) incomplete discrete domain models that have possible, rather than known, preconditions and effects in action descriptions; and (2) approximate continuous domain models, where the transition function is approximated from past observations and not well- defined. We develop novel goal recognition approaches over imperfect domains models by leveraging and adapting existing recognition approaches from the literature. Experiments and evalu...

2021

Goal recognition is the problem of inferring the correct goal towards which an agent executes a plan, given a set of goal hypotheses, a domain model, and a (possibly noisy) sample of the plan being executed. This is a key problem in both cooperative and competitive agent interactions and recent approaches have produced fast and accurate goal recognition algorithms. In this paper, we leverage advances in operator-counting heuristics computed using linear programs over constraints derived from classical planning problems to solve goal recognition problems. Our approach uses additional operator-counting constraints derived from the observations to efficiently infer the correct goal, and serves as basis for a number of further methods with additional constraints.

2020

Recent approaches to goal and plan recognition using classical planning domains have achieved state of the art results in terms of both recognition time and accuracy by using heuristics based on planning landmarks. To achieve such fast recognition time these approaches use efficient, but incomplete, algorithms to extract only a subset of landmarks for planning domains and problems, at the cost of some accuracy. In this paper, we investigate the impact and effect of using various landmark extraction algorithms capable of extracting a larger proportion of the landmarks for each given planning problem, up to exhaustive landmark extraction. We perform an extensive empirical evaluation of various landmark-based heuristics when using different percentages of the full set of landmarks. Results show that having more landmarks does not necessarily mean achieving higher accuracy and lower spread, as the additional extracted landmarks may not necessarily increase be helpful towards the goal re...

2018

Plan recognition is the task of inferring the plan of an agent, based on an incomplete sequence of its observed actions. Previous formulations of plan recognition commit early to discretizations of the environment and the observed agent’s actions. This leads to reduced recognition accuracy. To address this, we first provide a formalization of recognition problems which admits continuous environments, as well as discrete domains. We then show that through mirroring— generalizing plan-recognition by planning—we can apply continuous-world motion planners in plan recognition. We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical

Elsevier eBooks, 1994