Plan recognition is  one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber security. We focus on a class of algorithms that use plan libraries as input to the recognition process. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared to each other on common test bed. This paper directly addresses this gap by providing a standard plan library representation and evaluation criteria to consider. Our representation is comprehensive enough to describe a variety of known plan recognition problems, yet it can be easily applied to existing algorithms, which can be evaluated using our defined criteria. We demonstrate this technique on two known algorithms, SBR and PHATT. We provide meaningful insights both about the differences and abilities of the algorithms. We show that SBR is superior to PHATT both in terms of computation time and space, but at the expense of functionality and compact representation. We also show that depth is the single feature of a plan library that increases the complexity of the recognition, regardless of the algorithm used.

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this work, we develop a goal recognition technique capable of recognizing goals using incomplete (and possibly incorrect) domain theories.

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 planning domains.

This paper defines a learning algorithm for plan grammars used for plan recognition. The algorithm learns Combinatory Categorial Grammars (CCGs) that capture the structure of plans from a set of successful plan execution traces paired with the goal of the actions. This work is motivated by past work on CCG learning algorithms for natural language processing, and is evaluated on five well know planning domains.

Scenario planning is a commonly used method by companies to develop their long-term plans. Scenario planning for risk management puts an added emphasis on identifying and managing emerging risk. While a variety of methods have been proposed for this purpose, we show that applying AI planning techniques to devise possible scenarios provides a unique advantage for scenario planning. Our system, the Scenario Planning Advisor (SPA), takes as input the relevant information from news and social media, representing key risk drivers, as well as the domain knowledge and generates scenarios that explain the key risk drivers and describe the alternative futures. To this end, we provide a characterization of the problem, knowledge engineering methodology, and transformation to planning. Furthermore, we describe the computation of the scenarios, lessons learned, and the feedback received from the pilot deployment of the SPA system in IBM.

Modern multicore computers provide an opportunity to parallelize plan-recognition algorithms to decrease run time. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009, Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.

This article presents new algorithms for inferring users' activities in a class of flexible and open-ended educational software called exploratory learning environments (ELEs). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. This article presents techniques for recognizing students' activities in ELEs and visualizing these activities to students. It describes a new plan-recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries.

Intentions in Communication is the outgrowth of an interdisciplinary workshop on the role of intention in theories of communication. Attending the workshop were researchers in computer science, linguistics, philosophy, and psychology. The resulting book contains edited versions of 14 papers (13 of which were presented at the workshop), commentaries, and an introduction. The topics of these papers range from philosophical analyses of the concept of intention to algorithms for recognizing plans, from logical formalizations of speech acts to analyses of intonational contours in discourse. The idea of relating intentions to the use of language is an outgrowth of speech act theory.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Our approach is designed to take the noisy results of traditional data-mining tools and exploit causal knowledge about attacks to relate activities and uncover the intent underlying them. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.

My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans. This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconceptions, which categorizes all ways that a plan can fail, and I have developed a probabilistic interpretation of these misconceptions that can be used in principle to guide a bestfirst-search algorithm. I have also developed a program called Pathfinder that embodies a practical implementation of this theory. Pathfinder is a probability-based plan-recognition system based on the A* algorithm that uses information available from a user model to guide a bestfirst search through a plan hierarchy.