We define a notion of bounded action theory in the situation calculus, where the theory entails that in all situations, the number of ground fluent atoms is bounded by a constant. Such theories can still have an infinite domain and an infinite set of states. We argue that such theories are fairly common in applications, either because facts do not persist indefinitely or because one eventually forgets some facts, as one learns new ones. We discuss various ways of obtaining bounded action theories. The main result of the paper is that verification of an expressive class of first-order $\mu$-calculus temporal properties in such theories is in fact decidable. This paper is an abridged version of a paper appeared in KR'12.

Efforts by the pervasive, context-aware system development community have over the years produced a wide variety of context-aware techniques and frameworks. However, a bulk of this technology tends to be strictly tied to a native system, thus largely limiting its external adoption. In addressing this limitation, we introduce an interoperable context representation format, in the form of an ontology, which models core context-aware concepts for re-use within pervasive computing environments. The DCON Context Ontology is proposed as a novel vocabulary for the representation of activity context as experienced by a user, and sensed through one or more of their devices. We demonstrate how, combined with other domain ontologies, DCON provides for richer representations of multi-level context interpretations that are integrated with other known background information about a user.

Non intrusive activity recognition systems typically read values from sensors deployed in an environment and combine them with user annotated activities to build a probabilistic model. Recently, features constructed from activity specific conjunctions of binary sensor values have been shown to improve the classification accuracy. Such systems employ greedy feature induction techniques to find the observation features and combine them with state transition distribution in a Hidden Markov Model or a Conditional Random Field. An exhaustive search for optimum features is infeasible in this exponential feature space. We have recently extended the rule ensemble learning using hierarchical kernels (RELHKL) framework, that learns a sparse set of simple features and their optimum weights, to structured output spaces for learning optimum observation features along with the transition features and their weights. The exponentially large space of conjunctions is handled efficiently by exploiting its hierarchical structure. Our experiments have shown good improvement over other approaches. Although such approaches solve propositional classification problems optimally, their first-order extension is non-trivial and is a challenging problem. In this paper, we discuss about the challenges involved in leveraging the RELHKL in structured output spaces approach to learn optimum features in complex first order activity recognition settings.

The core business of Mobile Network Operators (MNO) has moved from network management and phone services to service providing. In contrast to Information Communication Technology (ICT) service providers, MNOs handle large amounts of their customers’ context data and generated content, which can be used to bring value-added services to customers and therefore, generate solid revenues. Given this scenario, this paper describes how Telecom Italia (a major Italian MNO) has prototyped such type of services after a deep research performed in the context-awareness and context management field and using its user-generated content management facilities in federation with other platforms and systems.

Recognizing social interests plays an important role of aiding human-computer interaction and human collaborative works. The recognition of social interest could be of great help to determine the smoothness of the interaction, which could be an indicator for group work performance and relationship. From socio-psychological theories, social engagement is the observable form of inner social interest, and represented as patterns of turn-taking and speech emotion during a face-to-face conversation. With these two kinds of features, a multi-layer learning structure is proposed to model the continuous trend of engagement. The level of engagement is classified into “high” and “low” two levels according to human-annotated score. In the result of assessing two-level engagemet, the highest accuracy of our model can reach 79.1%.

Public sensing is a new research area in the fields of wireless sensor networks and mobile computing. It leverages the mobile sensors and system resources readily available in mobile phones to execute sensing tasks. In order to plan, execute and adapt large-scale sensing tasks, applications need to query for the available resources, e.g. the density of certain sensors. We investigate how such information can be provided, and we propose a resource manager for public sensing. Our primary goal is to minimize the energy consumed by the mobile devices to make public sensing feasible without disturbing users. We propose a cluster-based protocol for collecting local views of the resource state using local ad-hoc communication since this is much more energy-efficient than long-range (e.g. cellular) communication. We compare our solution to a standard approach where mobile devices communicate their resource states using the cellular phone network. We show that 65% of the energy is saved and the communication load on the infrastructure is reduced by 90% while an average delivery ratio of 93% is retained.

We have explored with three notions: conceptualization, and contextualization from situated cognition, and psychic reflection from activity theory for identifying activities into a method called the activity states framework (ASF). The purpose of our work is to build an AI system based on ASF for the identification of collaborators activities during situated context, e.g., collaborators are engaged in a tutorial activity. In this paper, we will introduce and propose how Web-mediated collaborative activities can be identified from collaborators communication exchanges by applying the ASF.

Pervasive, context-aware computing technologies are essential drivers of next generation applications and appliances that will profoundly impact the way we work and play, conduct research, impart education, govern ourselves and care for our health. In order to support context-aware applications and achieve multidevice interoperability, it is important to develop an effective framework and representation language(s) for capturing and representing activity and context information, reasoning about the information and moving such information across devices in a secure and efficient manner. Our intent was as follows: First, discuss and review existing and novel Activity Context Representation and Exchange Languages. Discuss results from creation of solution architectures and proposals for languages, data structures, operations to enable top use-case categories. Second, discuss papers and proposals for new research areas and review work building on key research themes with specific opportunities for collaborative work in the next two-three years in this academically and commercially important area, with topics including, but not limited to semantic computing, task modeling, context representation, and activity recognition.

This paper suggests some teaching strategies for Algorithms and AI courses. These courses can have a common goal of teaching complex problem-solving techniques. Based on my experience teaching undergraduates in a small liberal-arts college, the paper offers concrete ideas for working toward this goal. These ideas are supported by relevant studies in cognitive science and education. Together, they provide a plan for structuring lessons and assignments to help student become better problem-solvers.

Virtual worlds present a challenge for intelligent mobile agents. They are required to generate maps of very large scale, dynamic and unstructured environments in a short amount of time. We investigate how to represent maps of ever growing virtual environments, how the agent can build, update and use these maps to navigate between points in the environment. We look at trails, the movement of other people and agents in the environment as a new information source. We can use trails to improve the generation of probabilistic roadmaps in these environments and enable the agent to segment space intelligently. Our future plans are to extend this to look at dynamic environments, where the agent will have to recognise change and update the map and how this will affect the map representation.