In this paper we explore challenges related to the engineering of plan libraries for plan recognition. This is an often overlooked problem, yet essential in the design of any real world plan recognizers. We mainly discuss challenges related to the evaluation of equivalence between plan libraries. We explain why this is a potential source of ill-conceived plan libraries. We consider an existing well-established probabilistic plan recognizer as vehicle for our discussion, using the formalism of probabilistic hierarchical task networks to represent plans. We propose avenues for exploring solutions to those challenges within that framework.

Activity recognition plays an important role in many areas such as smart environments by offering unprecedented opportunities for assisted living, automation, security and energy efficiency. It’s also an essential component for planning and plan recognition in smart environments. One challenge of activity recognition is the need for collecting and annotating huge amounts of data for each new physical setting in order to be able to carry out the conventional activity discovery and recognition algorithms. This extensive initial phase of data collection and annotation results in a prolonged installation process and excessive time investment for each new space. In this paper we propose a new method of transferring learned knowledge of activities to a new physical space in order to leverage the learning process in the new environment. Our method called ”Home to Home Transfer Learning” (HHTL) is based on using a semi EM framework and modeling activities using structural, temporal and spatial features. This method allows us to avoid the tedious task of collecting and labeling huge amounts of data in the target space, and allows for a more accelerated and more scalable deployment cycle in the real world. It also allows us to exploit the insights learned in previous spaces. To validate our algorithms, we use the data collected in several smart apartments with different physical layouts.

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates, the change of fluents and the calculation of durations by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into forward-chaining planning systems and report on our experience of adding this extension to the planner Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.

In this paper we outline an approach to the integration of task planning and motion planning that has the following key properties: It is aggressively hierarchical. It makes choices and commits to them in a top-down fashion in an attempt to limit the length of plans that need to be constructed, and thereby exponentially decrease the amount of search required. Importantly, our approach also limits the need to project the effect of actions into the far future. It operates on detailed, continuous geometric representations and partial symbolic descriptions. It does not require a complete symbolic representation of the input geometry or of the geometric effect of the task-level operations.

In this paper we apply a Monte-Carlo Tree Search implementation that is boosted with domain knowledge to the game of poker. More specifically, we integrate an opponent model in the Monte-Carlo Tree Search algorithm to produce a strong poker playing program. Opponent models allow the search algorithm to focus on relevant parts of the game-tree. We use an opponent modelling approach that starts from a (learned) prior, i.e., general expectations about opponent behavior, and then learns a relational regression tree-function that adapts these priors to specific opponents. Our modelling approach can generate detailed game features or relations on-the-fly. Additionally, using a prior we can already make reasonable predictions even when limited experience is available for a particular player. We show that Monte-Carlo Tree Search with integrated opponent models performs well against state-of-the-art poker programs.

Providing cognitive assistance in smart homes is a field of research that receives a lot of attention lately. In order to give adequate assistance at the opportune moment, we need to recognize the observed behavior when the patient carries out some activities in a smart home. To address this challenging issue, we present a formal activity recognition framework based on possibility theory. We present initial results from an implementation of this possibilistic recognition approach in a smart home laboratory.

Humans somehow manage to choose quite intelligently planner should fill in to produce a concrete plan that accomplishes the 20 trillion primitive motor commands that constitute a the goal as quickly as possible. It has long been thought that hierarchical structure in Planning at multiple levels of abstraction has long been a behavior is essential in managing this complexity. For instance, Shakey the exists at many levels, ranging from small (hundred-step?) robot used STRIPS for high-level task planning, then called motor programs for typing characters and saying phonemes out to separate low-level planning/control algorithms to execute up to large (billion-step?) actions such as writing an ICAPS each of the planned actions (Fikes and Nilsson 1971). This hard separation of levels, where a high-level plan is We believe that leveraging hierarchical structure will be chosen before considering low-level details, greatly simplifies equally important in achieving robust, efficient robotic behaviors. However, the resulting plans While your household robot probably won't get may be inefficient or even infeasible due to missed lowerlevel tenure anytime soon, even simple domestic tasks still have synergies and conflicts.

Previous work on the YAPPR plan recognition system provided algorithms for translating conventional HTN plan libraries into lexicalized grammars and treated the problem of plan recognition as one of parsing. To produce these grammars required a fixed bound for any loops within the grammar and a presented a problem for optional actions within HTN plans. In this work we show that well known transformations from formal language theory can be used to rewrite the plan grammars to remove these limitations on the plan libraries.

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. Recently, we have introduced a model for MAPR with a flat library structure, to study the complexity of basic MAPR, and also possibly its extensions in the future. Interestingly, this model makes fewer assumptions than existing models, and hence is more general. Therefore, as no existing algorithm would apply to this model, we have developed an hypothesis generation algorithm for this model, and adapted Knuth's Algorithm X for branch and bound search in the resulting hypothesis space. In this paper, we establish the time complexity of hypothesis generation in this model, propose and evaluate 3 different bounding criteria, and also empirically study the dependence of runtimes (hypothesis generation, and search times separately) on the model parameters.

In Real-Time Strategy (RTS) video games, players (controlled by humans or computers) build structures and recruit armies, fight for space and resources in order to control strategic points, destroy the opposing force and ultimately win the game. Players need to predict where and how the opponents will strike in order to best defend themselves. Conversely, assessing how the opponents will defend themselves is crucial to mounting a successful attack while exploiting the vulnerabilities in the opponent's defence strategy. In this context, to be truly adaptable, computer-controlled players need to recognize their opponents' behaviour, their goals, and their plans to achieve those goals. In this paper we analyze the algorithmic challenges behind behaviour recognition in RTS games and discuss a generic RTS behaviour recognition system that we are developing to address those challenges. The application domain is that of RTS games, but many of the key points we discuss also apply to other video game genres such as multiplayer first person shooter (FPS) games.