Context personalisation is a flourishing area of research with many applications. Context personalisation systems usually employ a user model to predict the appeal of the context to a particular user given a history of interactions. Most of the models used are context-dependent and their applicability is usually limited to the system and the data used for model construction. Establishing models of user experience that are highly scalable while maintaing the performance constitutes an important research direction. In this paper, we propose generic models of user experience in the computer games domain. We employ two datasets collected from players interactions with two games from different genres where accurate models of players experience were previously built. We take the approach one step further by investigating the modelling mechanism ability to generalise over the two datasets. We further examine whether generic features of player behaviour can be defined and used to boost the modelling performance. The accuracies obtained in both experiments indicate a promise for the proposed approach and suggest that game-independent player experience models can be built.

How can robots fluently communicate with humans and have context-preserving conversation? It is the most momentous and crucial problem in robotics research, especially for service robots such as child-care robots. Here, we aim to develop a situated conversation system for child-care robots. The conversation system considers the current context between robots and children as well as the situation the child is in. The system consists of two parts. The first part tries to understand the context. This part uses the embedded sensors of robots to understand the context and wearable sensors of the child for getting information of the situation the child is in. The second part is to generate the situated conversation. In terms of the model, we designed a hierarchical Bayesian Network for the first part and a Hypernetwork model is used for the second part. We illustrate the application of communication with a child in a child-care service robots scenario. For this application, we collect wearable sensors’ data from the child and mother-child conversation data in daily life. Finally, we discuss our results and future works.

Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.

The number of applications of service robotics in public spaces such as hospitals, museums and malls is a growing trend. Public spaces, however, provide several challenges to the robot, and specifically with its planning capabilities: they need to cope with a dynamic and uncertain environment and are subject to particular human-robot interaction constraints. A major challenge is the Joint Intention problem. When cooperating with humans, a persistent commitment to achieve a shared goal cannot be always assumed, since they have an unpredictable behavior and may be distracted in environments as dynamic and uncertain as public spaces, and even more so if the human agents are customers,visitors or bystanders. In order to address such issues in a decision-making context, we present a framework based on Hierarchical Factored POMDPs. We describe the general method for ensuring the Joint Intention between human and robot , the hierarchical structure and the Value Decomposition method adopted to build it.We also provide an example application scenario: an Escort Task in a shopping mall for guiding a customer towards a desired point of interest.

In order for robots to interact with humans in the world around them, it is important that they are not just aware of the presence of people, but also able to understand what those people are doing. In particular, interaction involves multiple agents which requires some form of coordination, and this cannot be achieved by acting blindly. The field of plan recognition (PR) studies methods for identifying an observed agent’s task or goal given her action sequence. This is often regarded as the inverse of planning which, given a set of goal conditions, aims to derive a sequence of actions that will achieve the goals when performed from a given initial state. Ram´ırez and Geffner (2009; 2010) proposed a simple transformation of PR problems into classical planning problems for which off-the-shelf software is available for quick and efficient implementations. However, there is a reliance on the observed agent’s optimality which makes this PR technique most useful as a post-processing step when some of the final actions are observed. In human-robot interaction (HRI), it is usually too late to interact once the humans are finished performing their tasks. In this paper, we describe ongoing work two extensions to make classical planning-based PR more applicable to the field of HRI. First, we introduce a modification to their algorithm that reduces the optimality bias’s effect so that long-term goals may be recognized at earlier observations. This is then followed by methods for extracting information from these predictions so that the observing agent may run a second pass of the planner to determine its own actions to perform for a fully interactive system.

GHOST is a framework to help game developers to model and implement their own optimization problems, or to simply instantiate a problem already encoded in GHOST. Previous works show that GHOST leads to high-quality solutions in some tens of milliseconds for three RTS-related problems: build order, wall-in placement  and target selection. In this paper, we show the robustness of the framework, having very good results for a problem it is not designed for (pathfinding), as well as its flexibility, where it is easy to propose different models of the same problem (resource allocation problem). The goal of the paper is not to improve the state-of-the-art on these problems, but to use them as benchmarks to test GHOST properties.

A key contribution of this paper is to introduce an extended BO setting, called Bayesian Optimization with Resources We consider optimizing an unknown function f by running (BOR), that explicitly models experimental resources experiments that each take an input x and return a noisy output and activities. In particular, our model specifies f(x). In particular, we focus on the setting where experiments the following: 1) resource requirements for experiments, are expensive, limiting the number of experiments which may vary across different experiments, 2) resourceproduction that can be run. Bayesian Optimization (BO) addresses this actions, which produce the various resources and setting by maintaining a Bayesian posterior over f to capture can require varying amounts of time, and 3) a set of "labs" our uncertainty about f given prior experiments (Jones for running concurrent experiments and a set of "production 2001; Brochu, Cora, and de Freitas 2010). The posterior is lines" for concurrent resource production. The problem is then used to select new experiments that trades-off exploring then to select and schedule the experiments and resourceproduction uncertain areas of the experimental space and exploiting actions in order to optimize the unknown objective promising areas.

There has been a large body of research demonstrating that students that receive one-on-one tutoring perform, on average, significantly better than students learning via conventional classroom instruction when tested on the same material (Bloom 1984; VanLehn 2011). During tutoring, the teacher has the ability to tailor the instruction and support to the individual learner, creating a personalized learning environment for each student. Research involving robotic agents Figure 1: Child interacting with a NAO robot in a tutoring as tutors indicates that the physical presence of a robot tutor scenario can increase cognitive learning gains (Leyzberg et al. 2010). Further research shows that a robot tutor employing relatively simple personalization strategies can benefit the that on-demand help is useful in interactive learning environments learner (Leyzberg, Spaulding, and Scassellati 2014).

Motivation impacts people’s lives in a powerful way and is at the heart of a plethora of day-to-day activities and achievement settings, from success at the workplace to learning and acquiring knowledge to trying to quit bad habits. The current work aims to develop an adaptive robot companion that models a user’s daily motivational state and chooses appropriate motivational strategies to keep the user on track for achieving a daily goal. The two main components we are focusing on in this context are creating an ontology-based user model of the person’s motivational states and using an appropriate strategy selection algorithm that chooses the best motivational strategies for the user each day based on the user model’s output. Specifically, we are focusing on the important application domain of physical activity and aim to help early adolescents achieve daily-recommended levels of physical activity. Our human-robot interaction system uses information acquired from the user to feed the user model and physical activity data from a wristband device to inform the strategy selection algorithm.

We consider stochastic planning problems that involve multiple objectives such as minimizing task completion time and energy consumption. These problems can be modeled as multi-objective Markov decision processes (MOMDPs), an extension of the widely-used MDP model to handle problems involving multiple value functions. We focus on a subclass of MOMDPs in which the objectives have a {\em relaxed lexicographic structure}, allowing an agent to seek improvement in a lower-priority objective when the impact on a higher-priority objective is within some small given tolerance. We examine the relationship between this class of problems and {\em constrained MDPs}, showing that the latter offer an alternative solution method with strong guarantees. We show empirically that a recently introduced algorithm for MOMDPs may not offer the same strong guarantees, but it does perform well in practice.