Comparing Plan Recognition Algorithms Through Standard Plan Libraries

Plan recognition deals with reasoning about the goals and execution process of an actor, given observations of its actions. It is one of the fundamental problems of AI, applicable to many domains, from user interfaces to cyber-security. Despite the prevalence of these approaches, they lack a standard representation, and have not been compared using a common testbed. This paper provides a first step towards bridging this gap by providing a standard plan library representation that can be used by hierarchical, discrete-space plan recognition and evaluation criteria to consider when comparing plan recognition algorithms. This representation is comprehensive enough to describe a variety of known plan recognition problems and can be easily used by existing algorithms in this class. We use this common representation to thoroughly compare two known approaches, represented by two algorithms, SBR and Probabilistic Hostile Agent Task Tracker (PHATT). We provide meaningful insights about the differences and abilities of these algorithms, and evaluate these insights both theoretically and empirically. We show a tradeoff between expressiveness and efficiency: SBR is usually superior to PHATT in terms of computation time and space, but at the expense of functionality and representational compactness. We also show how different properties of the plan library affect the complexity of the recognition process, regardless of the concrete algorithm used. Lastly, we show how these insights can be used to form a new algorithm that outperforms existing approaches both in terms of expressiveness and efficiency.

A Survey on Goal Recognition as Planning

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 behavior. While research on this problem gathered momentum as an offshoot of plan recognition, recent research has established it as a major subject of research on its own, leading to numerous new approaches that both expand the expressivity of domains in which to perform goal recognition and substantial advances to the state-of-the-art on established domain types. In this survey, we focus on the advances to goal recognition achieved in the last decade, categorizing the resulting techniques and identifying a number of opportunities for further breakthrough research.

Plan recognition in dynamic 2-D environments

We look at the relatively unexplored problem of plan recognition applied to motion in 2-D environments where all moving objects are modelled as circles. Golog is a well-known high level logical language for solving planning problems and specifying agent controllers. Few studies have applied Golog to plan recognition. We use some of the features of this language, but its standard interpreter is adapted to solving plan recognition problems. This thesis makes several other contributions. First, plan recognition procedures are formulated as finite automata and expressed as Golog programs. Second, we elaborate a logical formalism for reasoning about depth and motion from an observer's viewpoint. We not only expand on this situation calculus based formalism, but also apply it to tackle plan recognition problems in the traffic domain. The proposed approach is implemented and thoroughly tested on recognizing simple behaviours such as left turns, right turns, and overtaking.

Plan recognition in dynamic 2-D environments

We look at the relatively unexplored problem of plan recognition applied to motion in 2-D environments where all moving objects are modelled as circles. Golog is a well-known high level logical language for solving planning problems and specifying agent controllers. Few studies have applied Golog to plan recognition. We use some of the features of this language, but its standard interpreter is adapted to solving plan recognition problems. This thesis makes several other contributions. First, plan recognition procedures are formulated as finite automata and expressed as Golog programs. Second, we elaborate a logical formalism for reasoning about depth and motion from an observer's viewpoint. We not only expand on this situation calculus based formalism, but also apply it to tackle plan recognition problems in the traffic domain. The proposed approach is implemented and thoroughly tested on recognizing simple behaviours such as left turns, right turns, and overtaking.

Activity, Plan, and Goal Recognition: A Review

Recognizing the actions, plans, and goals of a person in an unconstrained environment is a key feature that future robotic systems will need in order to achieve a natural human-machine interaction. Indeed, we humans are constantly understanding and predicting the actions and goals of others, which allows us to interact in intuitive and safe ways. While action and plan recognition are tasks that humans perform naturally and with little effort, they are still an unresolved problem from the point of view of artificial intelligence. The immense variety of possible actions and plans that may be encountered in an unconstrained environment makes current approaches be far from human-like performance. In addition, while very different types of algorithms have been proposed to tackle the problem of activity, plan, and goal (intention) recognition, these tend to focus in only one part of the problem (e.g., action recognition), and techniques that address the problem as a whole have been not so thoroughly explored. This review is meant to provide a general view of the problem of activity, plan, and goal recognition as a whole. It presents a description of the problem, both from the human perspective and from the computational perspective, and proposes a classification of the main types of approaches that have been proposed to address it (logic-based, classical machine learning, deep learning, and brain-inspired), together with a description and comparison of the classes. This general view of the problem can help on the identification of research gaps, and may also provide inspiration for the development of new approaches that address the problem in a unified way.

Inferring Agents’ Goals from Observing Successful Traces

Goal recognition is a sub-field of plan recognition that focuses on the goals of an agent. Current approaches in goal recognition have not yet tried to apply concept learning to a propositional logic formalism. In this paper, we extend our method for inferring an agent’s possible goal by observing this agent in a series of successful attempts to reach its goal and using concept learning on these observations. We propose an algorithm, LFST (Learning From Successful Traces), to produce concise hypotheses about the agent’s goal. We show that if such a goal exists, our algorithm always provides a possible goal for the agent, and we evaluate the performance of our algorithm in different settings. We compare it to another concept-learning algorithm that uses a formalism close to ours, and we obtain better results at producing the hypotheses with our algorithm. We introduce a way to use assumptions about the agent’s behavior and the dynamics of the environment, thus improving the agent’s goal deduction by optimizing the potential goals’ search space.