Many real world applications require managing both system requirements and user preferences where the latter are usually provided in a qualitative way. We introduce a new approach to handle these two aspects, in an efficient way, respectively through Constraint Satisfaction Problems (CSPs) and CP-nets. In particular, we use Arc Consistency (AC) in order to reduce the search space needed when looking for the optimal outcome in an acyclic CP-net. More precisely, assuming that there are always some shared variables between the CP-net and the CSP, our approach works by first applying AC to the CSP and then update the CP-net with the remaining variables values. The resulting simplified CP-net will then be used to look for the best outcome. Experimental tests conducted on randomly generated problem instances clearly show the effect of AC on the size of the search space and the time needed to find the best outcome.

The formal semantics of speech acts, even in the classical framework of illocutionary logic, requires considerations that go beyond individual speech activity and beyond the interpretation of individual sentences. We show how the formal semantics of speech acts can be extended to take into account the social effects and interactive aspects of illocutionary activity. To illustrate our approach, we focus on the semantics of assertions and descriptive discourse, contrasting the individual aspect of speaker's meaning and the epistemic effects of assertion making. The approach presented in this paper generalizes to all other types of illocutionary acts, adding specific content to the conversational record that registers the common ground of speakers and hearers as a dialogue unfolds.

Decision making has proposed multiple methods to help the decision maker in his analysis, by suggesting ways of formalization of the preferences as well as the assessment of the uncertainties. Although these techniques are established and proven to be mathematically sound, experience has shown that in certain situations we tend to avoid the formal approach by acting intuitively. Especially, when the decision involves a large number of attributes and outcomes, and where we need to use pragmatic and heuristic simplifications such as considering only the most important attributes and omitting the others. In this paper, we provide a model for decision making in situations subject to a large predictive uncertainty with a small learning sample. The high predictive uncertainty is concretized by a countably infinite number of prospects, making the preferences assessment more difficult. Our main result is an extension of the Maximum Entropy utility (MEU) principle into an asymptotic maximum entropy utility principle for preferences elicitation. This will allow us to overcome the limits of the existing MEU method to the extend that we focus on utility assessment when the set of the available discrete prospects is countably infinite. Furthermore, our proposed model can be used to analyze situations of high-cognitive load as well as to understand how humans handle these problems under Ceteris Paribus assumption.

The authors used the computational tool Coh-Metrix to examine expert writers’ paraphrases and in particular, how experts paraphrase text passages using condensing strategies. The overarching goal of this study was to develop machine learning algorithms to aid in the automatic detection of paraphrases and paraphrase types. To this end, three experts were instructed to paraphrase by condensing a set of target passages. The linguistic differences between the original passages and the condensed paraphrases were then analyzed using Coh-Metrix. The condensed paraphrases were accurately distinguished from the original target passages based on the number of words, word frequency, and syntactic complexity.

Protecting our nation’s ports is a critical challenge for homeland security and requires the research, development and deployment of new technologies that will allow for the efficient securing of shipments entering this country. Most approaches look only at statistical irregularities in the attributes of the cargo, and not at the relationships of this cargo to others. However, anomalies detected in these relationships could add to the suspicion of the cargo, and therefore improve the accuracy with which we detect suspicious cargo. This paper proposes an improvement in our ability to detect suspicious cargo bound for the U.S. through a graph-based anomaly detection approach. Using anonymized data received from the Department of Homeland Security, we demonstrate the effectiveness of our approach and its usefulness to a homeland security analyst who is tasked with uncovering illegal and potentially dangerous cargo shipments.

Maritime threat detection is a challenging problem because maritime environments can involve a complex combination of concurrent vessel activities, and only a small fraction of these may be irregular, suspicious, or threatening. Previous work on this task has been limited to analyses of single vessels using simple rule-based models that alert watchstanders when a proximity threshold is breached. We claim that Probabilistic Graphical Models (PGMs) can be used to more effectively model complex maritime situations. In this paper, we study the performance of PGMs for detecting (small boat) maritime attacks. We describe three types of PGMs that vary in their representational expressiveness and evaluate them on a threat recognition task using track data obtained from force protection naval exercises involving unmanned sea surface vehicles. We found that the best-performing PGMs can outperform the deployed rule-based approach on these tasks, though some PGMs require substantial engineering and are computationally expensive.

Most of preference representation languages developed in the literature are based on comparative preference statements. The latter offer a simple and intuitive way for expressing preferences. They can be interpreted following different semantics. This paper presents a postulate-based analysis of the different semantics describing their behavior w.r.t. three criteria: coherence, syntax independence and inference.

The dynamic between speaker intent and listener perception is played out in the variation of acoustical cues by the speaker that must be interpreted by the listener to determine in an appropriate way. Emotion speech research must rely on either acted intent (i.e., an actor attempting to express an emotion) or listener perception (i.e., listening tests to assign emotional categories to non-acted data) to define ground truth labels for analysis. The emotion labels are described either using emotion dimension or emotion category. This study examines the two emotion characterization strategies dimension and category in communication of emotion embedded in speech as expressed through acted intent and the perception of emotion determined by a group of listeners. The results reveal that, without context information, intended emotion categories could be perceived by listeners with the averaged accuracy rate five times of chance in category. Also, the trend of listener ratings between emotion dimensions (valence/arousal) and emotional word categories was shown to be well correlated. Furthermore, while listeners confused the specific identity of certain emotional expressions, they were generally very accurate at identifying the intended affective space of the actor as determined by intended valence and arousal.

Modeling skills are essential during the process of learning programming. ITS systems for modeling are typically hard to build due to the ill-definedness of most modeling tasks. This paper presents a system that can teach UML skills to novice programmers. The system is “simple and cheap” in the sense that it only requires an expert solution against which the student solutions are compared, but still flexible enough to accommodate certain degrees of solution flexibility and variability that are characteristic of modeling tasks. An empirical evaluation via a controlled lab study showed that the system worked fine and, while not leading to significant learning gains as compared to a control condition, still revealed some promising results.

Recently cross-document integration and reconciliation of extracted information became of interest to researchers in Arabic natural language processing. Given a set of documents $A$, we use Arabic morphological analysis, finite state machines, and graph transformations to extract named entities N a and relations R a expressed as edges in a graph G = ( N a, R a ). We use the same techniques to extract entities N b and relations R b from a separate set of documents B. We use G to disambiguate N b and R and we integrate the resulting entities back into G by annotating the nodes and edges in G with elements from N b . We apply our approach in an iterative manner. Our results show a significant increase in accuracy from 41% to 93% after applying this cross-document NLP methodology to hadith and biography documents.