In this paper, we present a programming paradigm for AI problem solving based on computational concepts drawn from Affective Computing. It is believed that emotions participate in human adaptability and reactivity, in behaviour selection and in complex and dynamic environments. We propose to define a mechanism inspired from this observation for general AI problem solving. To this purpose, we synthesize emotions as programming abstractions that represent the perception of the environment's state w.r.t. predefined heuristics such as goal distance, action capability,etc. We first describe the general architecture of this "emotion-oriented" programming model. We define the vocabulary that allows programmers to describe the problem to be solved (i.e. the environment), and the action selection function based on emotion abstractions (i.e. the agent's behaviours). We then present the runtime algorithm that builds emotions out of the environment, stores them in the agent's memory, and selects behaviours accordingly. We present the implementation of a classical labyrinth problem solver in this model. We show that the solutions obtained by this easy-to-implement emotion-oriented program are of good quality while having a reduced computational cost.

Artifact capabilities can play an important role in understanding human cognition. Over time humans learn to use artifacts, evolve the knowledge and combine acquired capabilities with others to form complex capabilities. In this study we present a hybrid ontology of artifacts to facilitate learning artifact capabilities. We develop a framework where agents simultaneously exploit a centralized artifact ontology in the environment and a distributed artifact ontology local to each agent. We demonstrate how both ontologies can be used by agents both in the artifact selection process and in learning artifact use. The local ontology serves as domain knowledge gained by the agent as it learns. We illustrate an example to show how an acquired artifact capability can be stored in an agent's local ontology for future use.

This paper presents a case study in combining Lego Mindstorms NXT with Matlab/Simulink to help students in an undergraduate Machine Learning course study genetic algorithm design and testing. The project uses the VU-LRT toolbox to enable students to access the hardware capabilities of the Mindstorms platform from within Matlab. The course's enrollment was comprised of students from several majors with a variety of programming backgrounds. The course is part of an interdisciplinary cognitive science concentration. We report on the VU-LRT toolbox, the considerations imposed by the diversity of the student population on the design of the laboratory module and student evaluations of the laboratory module.

Novel human-computer interfaces, for instance talking heads, can benefit from language understanding and generation techniques with big impact on user satisfaction. Dialoguebased intelligent tutoring systems require advanced dialogue processing, language understanding and generation components in order to assess students' natural language inputs and provide appropriate feedback. Moreover, language can facilitate human-computer interaction for the handicapped (no typing needed) and elderly leading to an ever increasing user base for computer systems. Some of the many areas emphasized by the ANLP track to include for contributions include multilingual processing, learning environments, multimodal communication, bioNLP, spam filtering, language acquisition (first and second), textual assessment, language varieties, materials development, generic classification, educational applications, information retrieval, speech processing, machine learning, knowledge representations, English for specific purposes, textual assessment indices, coreference resolution, word sense disambiguation, dialogue management and systems, language generation, language models, ontologies, and reasoning. For 2012, there were 15 submissions, out of which 10 were accepted as long papers and 3 as poster presentations.

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.