In this paper we introduce a novel approach for unsupervised classification of planning instances based on the recent formalism of planning programs. Our approach is inspired by structured prediction in machine learning, which aims at predicting structured information about a given input rather than a scalar value. In our case, each input is an unlabelled classical planning instance, and the associated structured information is the planning program that solves the instance. We describe a method that takes as input a set of planning instances and outputs a set of planning programs, classifying each instance according to the program that solves it. Our results show that automated planning can be successfully used to solve structured unsupervised classification tasks, and invites further exploration of the connection between automated planning and structured prediction.

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.

Goal Recognition Design (GRD) is the problem of designing a domain in a way that will allow easy identification of agents' goals. This work extends the original GRD problem to the Plan Recognition Design (PRD) problem which is the task of designing a domain using plan libraries in order to facilitate fast identification of an agent's plan. While GRD can help to explain faster which goal the agent is trying to achieve, PRD can help in faster understanding of how the agent is going to achieve its goal. We define a new measure that quantifies the worst-case distinctiveness of a given planning domain, propose a method to reduce it in a given domain and show the reduction of this new measure in three domains from the literature.

Interaction between multiple agents requires some form of coordination and a level of mutual awareness. When computers and robots interact with people, they need to recognize human plans and react appropriately. Plan and goal recognition techniques have focused on identifying an agent's task given a sufficiently long action sequence. However, by the time the plan and/or goal are recognized, it may be too late for computing an interactive response. We propose an integration of planning with probabilistic recognition where each method uses intermediate results from the other as a guiding heuristic for recognition of the plan/goal in-progress as well as the interactive response. We show that, like the used recognition method, these interaction problems can be compiled into classical planning problems and solved using off-the-shelf methods. In addition to the methodology, this paper introduces problem categories for different forms of interaction, an evaluation metric for the benefits from the interaction, and extensions to the recognition algorithm that make its intermediate results more practical while the plan is in progress.

Plan recognition aims to recognize target plans given observed actions with history plan libraries ordomain models in hand. Despite of the success of previous plan recognition approaches, they all neglect the impact of human preferences on plans. For example, a kid in a shopping mall might prefer to "executing'' a plan of playing in water park, while an adult might prefer to "executing'' a plan of having a cup of coffee. It could be helpful for improving the plan recognition accuracy to consider human preferences on plans. We assume there are historical rating scores on a subset of plans given by humans, and action sequences observed on humans. We estimate unknown rating scores based on rating scores in hand using an off-the-shelf collaborative filtering approach. We then discover plans to best explain the estimated rating scores and observed actions using a skip-gram based approach. In the experiment, we evaluate our approach in three planning domains to demonstrate its effectiveness.

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.

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.

In goal recognition, the basic problem domain consists of the following: Recent advances in robotics and artificial intelligence have brought a variety of assistive robots designed to help humans - a set E of environment fluents; accomplish their goals. However, many have limited autonomy and lack the ability to seamlessly integrate with - a state S that is a value assignment to those fluents; human teams. One capability that can facilitate such humanrobot - a set A of actions that describe potential transitions between teaming is the robot's ability to recognize its teammates' states (with preconditions and effects defined over goals, and react appropriately. This function permits E, and parameterized over a set of environment objects the robot to actively assist the team and avoid performing O); and redundant or counterproductive actions.

We propose a new probabilistic plan recognition algorithm YR based onan extension of Tomita's Generalized LR (GLR) parser for grammarsenriched with the shuffle operator. YR significantly outperformsprevious approaches based on top down parsers, shows more consistentrun times among similar libraries, and degrades more gracefully asplan library complexity increases. YR also lifts the restrictions onleft-recursion imposed by approaches based on top-down parsingalgorithms. We further propose that context-free shuffle grammars,more than traditional context-free grammars, should be seen as theappropriate analogue of HTN plan libraries in the correspondence ofplan recognition and parsing.

Plan Recognition is the problem of inferring the goals and plans of an agent given a set of observations. In Multi-Agent Plan Recognition (MAPR) the task is extended to inferring the goals and plans of multiple agents. Previous MAPR approaches have largely focused on recognizing team structures and behaviors, given perfect and complete observations of the actions of individual agents. However, in many real-world applications of MAPR, observations are unreliable or missing; they are often over properties of the world rather than actions; and the observations that are made may not be explainable by the agents' goals and plans. Moreover, the actions of the agents could be durative or concurrent. In this paper, we address the problem of MAPR with temporal actions and with observations that can be unreliable, missing or unexplainable. To this end, we propose a multi-step compilation technique that enables the use of AI planning for the computation of the posterior probabilities of the possible goals. In addition, we propose a set of novel benchmarks that enable a standard evaluation of solutions that address the MAPR problem with temporal actions and such observations. We present results of an experimental evaluation on this set of benchmarks, using several temporal and diverse planners.