Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States

Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

SMIL State: an architecture and implementation for adaptive time-based web applications

In this paper we examine adaptive time-based web applications (or presentations). These are interactive presentations where time dictates which parts of the application are presented (providing the major structuring paradigm), and that require interactivity and other dynamic adaptation. We investigate the current technologies available to create such presentations and their shortcomings, and suggest a mechanism for addressing these shortcomings. This mechanism, SMIL State, can be used to add user-defined state to declarative time-based languages such as SMIL or SVG animation, thereby enabling the author to create control flows that are difficult to realize within the temporal containment model of the host languages. In addition, SMIL State can be used as a bridging mechanism between languages, enabling easy integration of external components into the web application. Finally, SMIL State enables richer expressions for content control. This paper defines SMIL State in terms of an introductory example, followed by a detailed specification of the State model. Next, the implementation of this model is discussed. We conclude with a set of potential use cases, including dynamic content adaptation and delayed insertion of custom content such as advertisements. © 2009 Springer Science+Business Media, LLC

Logic, Probability and Action: A Situation Calculus Perspective

The unification of logic and probability is a long-standing concern in AI, and more generally, in the philosophy of science. In essence, logic provides an easy way to specify properties that must hold in every possible world, and probability allows us to further quantify the weight and ratio of the worlds that must satisfy a property. To that end, numerous developments have been undertaken, culminating in proposals such as probabilistic relational models. While this progress has been notable, a general-purpose first-order knowledge representation language to reason about probabilities and dynamics, including in continuous settings, is still to emerge. In this paper, we survey recent results pertaining to the integration of logic, probability and actions in the situation calculus, which is arguably one of the oldest and most well-known formalisms. We then explore reduction theorems and programming interfaces for the language. These results are motivated in the context of cognitive robotics (as envisioned by Reiter and his colleagues) for the sake of concreteness. Overall, the advantage of proving results for such a general language is that it becomes possible to adapt them to any special-purpose fragment, including but not limited to popular probabilistic relational models

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Learning Opportunities 2010/2011

The graduation requirements of the Illinois Mathematics and Science Academy are in concert with those maintained by the State of Illinois with additional requirements as established by the IMSA Board of Trustees. Each semester students must take a minimum of 2.5 credits and a maximum of 3.5 credits. One-semester classes generally receive .5 credits and two semester classes (e.g., World Languages) generally receive 1.0 credit. Most students will take between 5 and 7 academic classes per semester. Fine Arts, Wellness, and Independent Study courses do not count towards the 2.5 credit minimum. However, if a student wishes to take 3.5 credits of academic classes, he/she may choose to enroll in a Fine Arts or Independent Study course on a Pass/Fail basis (see below)

Learning Opportunities 2016/2017

The graduation requirements of the Illinois Mathematics and Science Academy are in concert with those maintained by the State of Illinois with additional requirements as established by the IMSA Board of Trustees. Each semester students must take a minimum of 5 academic courses (2.5 credits) for a grade (not Pass/Fail). Fine Arts, Wellness, and Independent Study courses, or any course taken on a Pass/Fail basis do not count towards the 5 course (2.5 credits) minimum. Most students will take between 5 (2.5 credits) and 7 (3.5 credits) academic courses per semester. Only courses taken for a letter grade will count towards graduation credit. Students who take more than 5 academic courses may choose to take all courses for a grade. It is recommended that students who are approved to take 7 academic courses declare one elective Pass/Fail