We introduce the notion of strategic uncertainty for boundedly rational, non-myopic agents as an analog to the equilibrium selection problem in classical game theory. We then motivate the need for and feasibility of addressing strategic uncertainty and present an algorithm that produces decisions that are robust to it. Finally, we show how agents' rationality levels and planning horizons alter the robustness of their decisions.

In this paper, we introduce new capabilities to our existing microsimulation framework, Simulacron. These new capabilities add the modelling of behaviours based on motivations and improve our existing non-deterministic movement capacity. We then discuss the application of these new features to a simple, synthetic, proof of concept, scenario involving the transit of people through a corridor and how an induced panic affects their throughput. Finally we describe a more complex scenario, which is currently under development, involving the detonation of an explosive device in a major metropolitan transport hub at peak hour and the analysis of subsequent reaction.

We propose a mechanism to learn communicative action structure to analyze adversarial reasoning patterns in customer complaints. An efficient way to assist customers and companies is to reuse previous experience with similar agents. A formal representation of customer complaints and a machine learning technique for handling scenarios of interaction between conflicting human agents are proposed. It is shown that analyzing the structure of communicative actions without context information is frequently sufficient to advise on complaint resolution strategies. Therefore, being domain-independent, the proposed machine learning technique is a good complement to a wide range of customer response management applications where formal treatment of inter-human interactions is required.

Speech acts provide good insights into the communicative behavior of tweeters on Twitter. This paper is mainly concerned with speech act recognition in Twitter as a multi-class classification problem, for which we propose a set of word-based and character-based features. Inexpensive, robust and efficient, our method achieves an average F1 score of nearly 0.7 with the existence of much noise in our annotated Twitter data. In view of the deficiency of training data for the task, we experimented extensively with different configurations of training and test data, leading to empirical findings that may provide valuable reference for building benchmark datasets for sustained research on speech act recognition in Twitter.

Automatic detection of persuasion is essential for machine interaction on the social web. To facilitate automated persuasion detection, we present a novel microtext corpus derived from hostage negotiation transcripts as well as a detailed manual (codebook) for persuasion annotation. Our corpus, called the NPS Persuasion Corpus, consists of 37 transcripts from four sets of hostage negotiation transcriptions. Each utterance in the corpus is hand annotated for one of nine categories of persuasion based on Cialdini’s model: reciprocity, commitment, consistency, liking, authority, social proof, scarcity, other, and not persuasive. Initial results using three supervised learning algorithms (Na ̈ve Bayes, Maximum Entropy, and Support Vector Machines) combined with gappy and orthogonal sparse bigram feature expansion techniques show that the annotation process did capture machine learnable features of persuasion with F-scores better than baseline.

We evaluate the statistical dependency parser, Malt, on a new dataset of sentences taken from tweets. We use a version of Malt which is trained on gold standard phrase structure Wall Street Journal (WSJ) trees converted to Stanford labelled dependencies. We observe a drastic drop in performance moving from our in-domain WSJ test set to the new Twitter dataset, much of which has to do with the propagation of part-of-speech tagging errors. Retraining Malt on dependency trees produced by a state-of-the-art phrase structure parser, which has itself been self-trained on Twitter material, results in a significant improvement. We analyse this improvement by examining in detail the effect of the retraining on individual dependency types.

Context Management technology is not novel itself, and ICT companies are already looking at this area and spending effort for a long time trying to find a technically feasible solution, appealing marketing usage and solve all the possible issues with its privacy and security concerns. However, after many years of technology scouting and academic scrutiny within this still innovating area, there is no unique best practice or reference standardization solving all the technological difficulties within this field. The context information available in the real world from many potential sources should be handled in a near real-time way, efficiently processed by many devices and be interoperable among different actors dealing with the context. Therefore not only a comprehensive context management framework shall be in the place but also efficient context representation formalism should be employed in order to represent the context data suitably for an autonomous Machine-to-Machine processing, with all the data maintained within that representation and with all the mechanisms or artifacts needed for a secure and privacy safeguarding sensitive data handling. This all compose a set of requirements to be respected in the context information data representation, which are listed and solved by the solution described within with paper.

Smart environments offer a vision of unobtrusive interaction with our surroundings, interpreting and anticipating our needs. One key aspect for making environments smart is the ability to recognize the current context. However, like any human space, smart environments are subject to changes and mutations of their purposes and their composition as people shape their living places according to their needs. In this paper we present an approach for recognizing context situations in smart environments that addresses this challenge. We propose a formalism for describing and sharing context states (or situations) and an architecture for gradually introducing contextual knowledge to an environment, where the current context is determined on sensing people's usage of devices and sound analysis.

In this paper, we study a time-critical facet of context-awareness: context transitions, which we model as changes in specific context types over time, e.g., activity or location. We present results from a user-centred field study involving participant interviews and motion data capture from two mobile device sensors: the accelerometer and magnetic field sensor. The results show how the participants subjectively interpret their daily context transitions with variable granularity, and a comparison of these context transitions with mobile device motion data shows how the motion data poorly reflect the identified transitions. The results imply that care should be taken when representing and modelling users’ subjective interpretations of context, as well as the objective nature of context sensors. Furthermore, processing and usability trade-offs should be made if real-time on-device transition detection is to be implemented.

Users are constantly involved in a multitude of activities in ever-changing context. Analyzing activities in context-rich environments has become a great challenge in context-awareness research. Traditional methods for activity recognition, such as classification, cannot cope with the variety and dynamicity of context and activities. In this paper, we propose an activity recognition approach that incorporates unsupervised learning. We analyze the feasibility of applying subspace clustering---a specific type of unsupervised learning — to high-dimensional, heterogeneous sensory input. Then we present the correspondence between clustering output and classification input. This approach has the potential to discover implicit, evolving activities, and can provide valuable assistance to traditional classification based methods.