The cognitive agent model presented in this paper generates  prior and retrospective ownership states for an action based on principles from recent neuro-logical theories. A prior ownership state is affected by prediction of the effects of a prepared action, and exerts control by strengthening or suppressing actual execution of the action. A retrospective ownership state depends on whether the sensed consequences co-occur with the predicted consequences, and is the basis for acknowledging authorship of actions, for example, in social context. It is shown how poor action effect prediction capabilities can lead to reduced retrospective ownership states, as in persons suffering from schizophrenia.

Differences in social responses of individuals can often be related to differences in functioning of neurological mechanisms. This paper presents a cognitive agent model capable of showing different types of social response patterns based on such mechanisms, adopted from theories on mirror neuron systems, emotion regulation, empathy, and autism spectrum disorders. The presented agent model provides a basis for human-like social response patterns of virtual agents in the context of simulation-based training (e.g., for training of therapists), gaming, or for agent-based generation of virtual stories.

Feature extraction for activity recognition in context-aware ubiquitous computing applications is usually a heuristic process, informed by underlying domain knowledge. Relying on such explicit knowledge is problematic when aiming to generalize across different application domains. We investigate the potential of recent machine learning methods for discovering universal features for context-aware applications of activity recognition. We also describe an alternative data representation based on the empirical cumulative distribution function of the raw data, which effectively abstracts from absolute values. Experiments on accelerometer data from four publicly available activity recognition datasets demonstrate the significant potential of our approach to address both contemporary activity recognition tasks and next generation problems such as skill assessment and the detection of novel activities.

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretical framework for incorporating domain knowledge in ASG. We complement the framework with empirical results in our existing system Riu.

Prediction models for multivariate spatio-temporal functions in geosciences are typically developed using supervised learning from attributes collected by remote sensing instruments collocated with the outcome variable provided at sparsely located sites. In such collocated data there are often large temporal gaps due to missing attribute values at sites where outcome labels are available. Our objective is to develop more accurate spatio-temporal predictors by using enlarged collocated data obtained by imputing missing attributes at time and locations where outcome labels are available. The proposed method for large gaps estimation in space and time (called LarGEST) exploits temporal correlation of attributes, correlations among multiple attributes collected at the same time and space, and spatial correlations among attributes from multiple sites. LarGEST outperformed alternative methods in imputing up to 80% of randomly missing observations at a synthetic spatio-temporal signal and at a model of fluoride content in a water distribution system. LarGEST was also applied for imputing 80% of nonrandom missing values in data from one of the most challenging Earth science problems related to aerosol properties. Using such enlarged data a predictor of aerosol optical depth is developed that was much more accurate than predictors based on alternative imputation methods when tested rigorously over entire continental US in year 2005.

In order for agents to be able to act intelligently in an environment, a first necessary step is to become aware of the current situation in the environment. Forming such awareness is not a trivial matter. Appropriate observations should be selected by the agent, and the observation results should be interpreted and combined into one coherent picture. Humans use dedicated mental models which represent the relationships between various observations and the formation of beliefs about the environment, which then again direct the further observations to be performed. In this paper, a generic agent model for situation awareness is proposed that is able to take a mental model as input, and utilize this model to create a picture of the current situation. In order to show the suitability of the approach, it has been applied within the domain of F-16 fighter pilot training for which a dedicated mental model has been specified, and simulations experiments have been conducted.

Given its ubiquity, scale and complexity, few problems have created the combined interest of so many unrelated areas as the evolution of cooperation. Using the tools of evolutionary game theory, here we address, for the first time, the role played by intention recognition in the final outcome of cooperation in large populations of self-regarding individuals. By equipping individuals with the capacity of assessing intentions of others in the course of repeated Prisoner's Dilemma interactions, we show how intention recognition opens a window of opportunity for cooperation to thrive, as it precludes the invasion of pure cooperators by random drift while remaining robust against defective strategies. Intention recognizers are able to assign an intention to the action of their opponents based on an acquired corpus of possible intentions. We show how intention recognizers can prevail against most famous strategies of repeated dilemmas of cooperation, even in the presence of errors. Our approach invites the adoption of other classification and pattern recognition mechanisms common among Humans, to unveil the evolution of complex cognitive processes in the context of social dilemmas.

It has been known for a long time that visual task, such as reading, counting and searching, greatly influences eye movement patterns. Perhaps the best known demonstration of this is the celebrated study of Yarbus showing that different eye movement trajectories emerge depending on the visual task that the viewers are given. The objective of this paper is to develop an inverse Yarbus process whereby we can infer the visual task by observing the measurements of a viewer’s eye movements while executing the visual task. The method we are proposing is to use Hidden Markov Models (HMMs) to create a probabilistic framework to infer the viewer’s task from eye movements.

Occlusions are a central phenomenon in multi-object computer vision. However, formal analyses (LOS14, ROC20) proposed in the spatial reasoning literature ignore many distinctions crucial to computer vision, as a result of which these algebras have been largely ignored in vision applications. Two distinctions of relevance to visual computation are (a) whether the occluder is a moving object or part of the static background, and (b) whether the visible part of an object is a connected blob or fragmented. In this work, we develop a formal model of occlusion states that combines these criteria with overlap distinctions modeled in spatial reasoning to come up with a comprehensive set of fourteen occlusion states, which we define as OCS14. Transitions between these occlusion states are an important source of information on visual activity (e.g. splits and merges). We show that the resulting formalism is representationally complete in the sense that these states constitute a partition of all possible occlusion situations based on these criteria. Finally, we show results from implementations of this approach in a test application involving static camera based scene analysis, where occlusion state analysis and multiple object tracking can be used for two tasks -- (a) identifying static occluders, and (b) modeling a class of interactions represented as transitions of occlusion states. Thus, the formalism is shown to have direct relevance to actual vision applications.

We report the results obtained during the verification of Autosub6000, an autonomous underwater vehicle used for deep oceanic exploration. Our starting point is the Simulink/Matlab engineering model of the submarine, which is discretised by a compiler into a representation suitable for model checking. We assess the ability of the vehicle to function under degraded conditions by injecting faults automatically into the discretised model. The resulting system is analysed by means of the model checker MCMAS, and conclusions are drawn on the system's ability to withstand faults and to perform self-diagnosis and recovery. We present lessons learnt from this and suggest a general method for verifying autonomous vehicles.