We provide an overview of the organization and results of the deterministic part of the 4th International Planning Competition, i.e., of the part concerned with evaluating systems doing deterministic planning. IPC-4 attracted even more competing systems than its already large predecessors, and the competition event was revised in several important respects. After giving an introduction to the IPC, we briefly explain the main differences between the deterministic part of IPC-4 and its predecessors. We then introduce formally the language used, called PDDL2.2 that extends PDDL2.1 by derived predicates and timed initial literals. We list the competing systems and overview the results of the competition. The entire set of data is far too large to be presented in full. We provide a detailed summary; the complete data is available in an online appendix. We explain how we awarded the competition prizes.

Metrics specifying distances between data points can be learned in a discriminative manner or from generative models. In this paper, we show how to unify generative and discriminative learning of metrics via a kernel learning framework. Specifically, we learn local metrics optimized from parametric generative models. These are then used as base kernels to construct a global kernel that minimizes a discriminative training criterion. We consider both linear and nonlinear combinations of local metric kernels. Our empirical results show that these combinations significantly improve performance on classification tasks. The proposed learning algorithm is also very efficient, achieving order of magnitude speedup in training time compared to previous discriminative baseline methods.

Discovering latent connections between seemingly unconnected documents and constructing "stories" from scattered pieces of evidence are staple tasks in intelligence analysis. We have worked with government intelligence analysts to understand the strategies they use to make connections. Beyond techniques like clustering that aim to provide an initial broad summary of large document collections, an important goal of analysts in this domain is to assimilate and synthesize fine grained information from a smaller set of foraged documents. Further, analysts' domain expertise is crucial because it provides rich contextual background for making connections and thus the goal of KDD is to augment human discovery capabilities, not supplant it. We describe a visual analytics system we have built - Analyst's Workspace (AW) - that integrates browsing tools with a storytelling algorithm in a large screen display environment. AW helps analysts systematically construct stories of desired fidelity from document collections and helps marshall evidence as longer stories are constructed.

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.

Ontologies are not only becoming a widespread formalism to create the knowledge base of current intelligent and semantic systems, but they are also suitable for modeling context information in ubiquitous applications, which require expressive representation and reasoning languages. In this paper, we discuss different approaches for ontological context management, as well as a proposal to represent and exploit significance-based relations with standard and fuzzy ontologies.

Attacker-defender Stackelberg games have become a popular game-theoretic approach for security with deployments for LAX Police, the FAMS and the TSA. Unfortunately, most of the existing solution approaches do not model two key uncertainties of the real-world: there may be noise in the defender’s execution of the suggested mixed strategy and/or the observations made by an attacker can be noisy. In this paper, we analyze a framework to model these uncertainties, and demonstrate that previous strategies perform poorly in such uncertain settings. We also analyze RECON, a novel algorithm that computes strategies for the defender that are robust to such uncertainties, and explore heuristics that further improve RECON’s efficiency.

Rather, the goal is to Microtext media (Ellen, 2011), such as SMS, IM, Twitter, gather relevant messages, organize them, and extract some and text chat, have in common that they use short strings other kind of useful information from them, such as how for immediate communication or broadcast. Microtext can well a team is performing or what people are talking about be construed as one form of micro-messaging (e.g., and when. However, micro-messages do not exist in a Milstein, et al., 2008) which we extend here to include any vacuum; they are contextually oriented and may be part of of a number of other modalities (e.g., telephone calls, a larger network of communications which includes email, face-to-face interaction) used for short, immediate and telephone and other media, including "macro-text." Given (potentially) persistent message passing among this, we have found that natural language processing of the coordinating agents. In this paper, we describe several microtext must be paired with temporal or network recent attempts to study micro-messaging military and analysis of the context. To demonstrate this process, we related organizational contexts.

Model update is a formal approach to correct a system model M w.r.t some property not satisfied by M. In this work, we show how this formal approach can be used for plan and planning domain verification and update. While a model checking method can directly be used to perform plan verification, model update techniques can be used to either update an incorrect plan and\or update a planning domain specification. Well known model update approaches are based on CTL — a logic which does not take into account the actions. In previous work, we have proposed the alpha-CTL logic, a logic whose semantics is based on actions. Here, we are proposing a model update system based on alpha-CTL which is able to automatically modify a plan M, generating a new plan M' that satisfies phi or, if there is not such a plan, to automatically update the corresponding planning domain.

Attacker-defender Stackelberg games have become a popular game-theoretic approach for security with deployments for LAX Police, the FAMS and the TSA. Unfortunately, most of the existing solution approaches do not model two key uncertainties of the real-world: there may be noise in the defender's execution of the suggested mixed strategy and/or the observations made by an attacker can be noisy. In this paper, we provide a framework to model these uncertainties, and demonstrate that previous strategies perform poorly in such uncertain settings. We also provide RECON, a novel algorithm that computes strategies for the defender that are robust to such uncertainties, and provide heuristics that further improve RECON's efficiency.

In this paper, we extend the progression semantics for first-order disjunctive logic programs and show that it coincides with the stable model semantics. Based on it, we further show how disjunctive answer set programming is related to Satisfiability Modulo Theories.