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.

Computer-based human activity recognition of daily living has recently attracted much interest due to its applicability to ambient assisted living. Such applications require the automatic recognition of high-level activities composed of multiple actions performed by human beings in an environment. In this work, we address the problem of activity recognition in an indoor environment, focusing on a kitchen scenario. Unlike existing approaches that identify single actions from video sequences, we also identify the goal towards which the subject of the video is pursuing. Our hybrid approach combines a deep learning architecture to analyze raw video data and identify individual actions which are then processed by a goal recognition algorithm that uses a plan library describing possible overarching activities to identify the ultimate goal of the subject in the video. Experiments show that our approach achieves the state-of-the-art for identifying cooking activities in a kitchen scenario.

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.

Recognizing human behavior from noisy and ambiguous sensor data is a prerequisite for many applications such as context-aware assistance. The sensor data, however, often do not allow to distinguish between multiple entities, e.g. a presence sensor does not allow to distinguish between two persons i.e. both are observation-equivalent. Conventional algorithms, however, consider each of these entities separately during the inference of human behavior, leading to a high computational burden in scenarios where a large number of entities have to be considered. Therefore, these algorithms can only be applied to very limited scenarios. We analyzed the challenges appearing in these scenarios and revealed that considering observation-equivalent entities separately is one reason for the huge computational effort. Thus, we propose to exploit observation-equivalence by representing entities as a group and inferring about these groups of entities. We sketch a mechanism that exploits observation-equivalencies which we call lifted probabilistic inference. To compare this approach with conventional inference approaches, we adapted an office scenario from the literature so that it parametrizes observation-equivalent entities and simulated a corresponding dataset. This dataset can be used as a benchmark for the evaluation of different inference approaches with respect to observation-equivalence. We compare the number of states this approach, and a conventional inference algorithm is considering during inference on this benchmark dataset. On average, the conventional approach uses almost 200,000 states to cover the situations of the scenario during the inference whereas our lifted probabilistic inference approach uses less than 100 states. Thus, an observation-equivalent approach seems promising for a more efficient inference in scenarios with many observation-equivalent entities.

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.

There are various knowledge-based activity recognition approaches that rely on manual definition of rules to describe user behaviour. These rules are later used to generate computational models of human behaviour that are able to reason about the user behaviour based on sensor observations. One problem with these approaches is that the manual rule definition is time consuming and error prone process. To address this problem, in this paper we outline an approach that learns the model structure from textual sources and later optimises it based on observations. The approach includes extracting the model elements and generating rules from textual instructions. It then learns the optimal model structure based on observations in the form of manually created plans and sensor data. The learned model can then be used to recognise the behaviour of users during their daily activities. We illustrate the approach with an example from the cooking domain.

We present a technique to further narrow the gap between recipe-based and domain theory-based plan recognition through decompositional planning, a planning model that combines hierarchical reasoning as used in hierarchical task networks, and least-commitment refinement reasoning as used in partial-order causal link planning. We represent recipes through decompositional planning operators and use them to compile observed agent actions into an incomplete decompositional plan that represents them; this plan can then be input to a decompositional planner to identify the recognized plan-space plan. Our model thus synthesizes the heretofore disparate recipe-based and domain theory-based plan recognition variants into a unified knowledge representation and reasoning model.

We consider the problem of actively eliciting preferences from a human by a planning system. While prior work in planning have explored the use of domain knowledge and preferences, they assume that the knowledge must be provided before the planner starts the planning process. Our work is in building more collaborative systems where a system can solicit advice as needed. We verify empirically that this approach lead to faster and better solutions, while reducing the burden on the human expert.

In 1960s and 70s Britain, immigrant ethnic minority children were dispersed across schools in the hope that it would help them integrate. The process saw children - largely of south Asian and African or Caribbean descent - being "bussed" out of their local areas to go to school. Eleven Local Area Authorities (LEAs) decided there should be no more than 30% of immigrants at any one school. It meant once that quota was reached, children were taken elsewhere. The process, which became known as "bussing", is now at the heart of a project in Bradford where Shabina Aslam is trying to trace children who, like herself, were sent to school away from where they lived.

For most people, the sound of wedding bells evokes happiness. Whether you're remembering the time you tied the knot with your significant other or finally jumping the broom, weddings are always a joyful celebration. But if you've already gone through the process, or are going through it now, you know just how untrue that emotion is when you're in the planning process. While I haven't had the pleasure of marrying my fiancé yet, I have had the pleasure of being a Maid of Honor quite a few times and am currently in the planning phases with my sister for her wedding – so I speak from experience when I say it's definitely not all sunshine and rainbows. Working with a budget, getting guest lists, picking venues and vendors is no easy task.