Next, through the Question Type Selection ArikIturri (Aldabe et al. 2006) is a system developed for the process, the question type is selected. Finally, by means automatic generation of different types of exercise. One of of the Question Construction step, the surface form of the the aims of ArikIturri is to generate items that could form question is created based on the previous steps. As regards part of real scenarios; this is why their creation is based our QG system, the sentence retriever module is responsible on topics that are part of the curriculum. Thus, the system for the Target Selection task and the item generator module is able to automatically generate tests from texts, to be included performs the Question Type Selection and Question Construction in testing tasks. The system is able to produce fill-inthe-blank processes.

Understanding population wide attitude change is an important step to understanding the behavior of societies. In this talk, we will study population wide attitude change through the use of computational models. Using a model based on parallel constraint satisfaction, we will show how varying parameters, such as cognitive effort, and community structure, can impact attitude change in populations.

Millions of people globally have been forcibly displaced from their homes due to reasons beyond their control such as conflict, political upheaval, and environmental catastrophes. In many cases, these forced migrants seek temporary refuge in camps managed by nongovernmental organizations (NGOs). Although responsibility for refugees’ well-being within camps belongs mainly to the NGOs and host government, the density of the camp population and lack of resources of service providers leads to a high degree of insecurity. Building off successful models of mHealth, or utilizing mobile technologies to address healthcare needs, this paper explores the possibility of using communication technologies to address personal security issues. Using agent based modeling techniques, this paper examines the ways in which information about incidents of violence are communicated through a closed population. In this way, the authors advocate for the use of mobile phones in an mSecurity context that empowers forced migrants to become active members in reducing incidents of violence within refugee and internally displaced persons camps.

International networks, although highly efficient, may produce surprising threshold effects that shift costs to geographically distant locations. International utility, transportation, and information networks facilitate the efficient flow of information, energy, goods and people. These networks exhibit a scale-free network structure with a few large “hubs”. Yet their efficiency belies their lack of robustness. Because such networks transcend national boundaries, furthermore, disruptions to the network in one geographic region may have profound economic and national security costs for countries in another region. To illustrate how complex networks may transmit costs among countries, this paper builds an agent-based model (ABM) of the international air transportation system. The ABM employs a genetic algorithm to identify “small” disruptions that produce cascading network failures. The study makes two contributions. First, it demonstrates how some complex networks evolve into network structures that trade off robustness for efficiency. Second, it illustrates how researchers can combine agent-based modeling, evolutionary computation, and network analysis to simulate differing failure modes for global networks. This convergence of simulation methodologies characterizes the emerging field of computational social science.

I ask why humans are smarter than other primates, and I hypothesize that an important part of the answer lies in what I call the Strong Story Hypothesis, which holds that story telling and understanding have a central role in human intelligence. Next, I introduce another hypothesis, the Driven Perception Hypothesis, which holds that we derive much of our commonsense, including the commonsense required in story understanding, by deploying our perceptual apparatus on real and imagined events. Then, after discussing methodology, I describe the representations and methods embodied in the Genesis system, a story-understanding system that analyzes stories ranging from precis of Shakespeare's plots to descriptions of conflicts in cyberspace. The Genesis system works with short story summaries, provided in English, together with low-level commonsense rules and higher-level reflection patterns, likewise expressed in English. Using only a small collection of commonsense rules and reflection patterns, Genesis demonstrates several story-understanding capabilities, such as determining that both Macbeth and the 2007 Russia-Estonia Cyberwar involve revenge, even though neither the word revenge nor any of its synonyms are mentioned. Finally, I describe Rao's Visio-Spatial Reasoning System, a system that recognizes activities such as approaching, jumping, and giving, and answers commonsense questions posed by Genesis.

Inference techniques play a central role in many cognitive systems. They transform low-level observations of the environment into high-level, actionable knowledge which then gets used by mechanisms that drive action, problem-solving, and learning. This paper presents an initial effort at combining results from AI and psychology into a pragmatic and scalable computational reasoning system. Our approach combines a numeric notion of plausibility with first-order logic to produce an incremental inference engine that is guided by heuristics derived from the psychological literature. We illustrate core ideas with detailed examples and discuss the advantages of the approach with respect to cognitive systems.

This paper proposes a novel agenda for cognitive systems research focused on the "social agency" problem, which concerns acting to produce mental states in other agents in addition to physical states of the world. The capacity for social agency will enable agents to perform a wide array of tasks in close association with people and is a valuable first step towards broader social cognition. We argue that existing cognitive systems have not addressed social agency because they lack a number of the required mechanisms. We describe an initial approach set in a toy scenario based on capabilities native to the ICARUS cognitive architecture. We utilize an analysis of this approach to highlight the open issues required for social agency and to encourage other researchers to address this important problem.

Cognitive systems with human-level intelligence must dis­play a wide range of abilities, including reasoning about the beliefs of others, hypothetical and future situations, quanti­fiers, probabilities, and counterfactuals. While each of these deals in some way with reasoning about alternative states of reality, no single knowledge representation framework deals with them in a unified and scalable manner. As a conse­quence it is difficult to build cognitive systems for domains that require each of these abilities to be used together. To enable this integration we propose a representational framework based on synchronizing beliefs between worlds. Using this framework, each of these tasks can be reformu­lated into a reasoning problem involving worlds. This demonstrates that the notions of worlds and inheritance can bring significant parsimony and broad new abilities to knowledge representation.

Desiderata for cognitive architectures that are to support the extent of human-level intelligence required in virtual humans imply the need to bridge a range of dichotomies faced by such architectures. The focus here is first on two general approaches to building such bridges — addition and reduction — and then on a pair of general tools – graphical models and piecewise continuous functions — that exploit the second approach towards developing such an architecture. Evaluation is in terms of the architecture’s demonstrated ability and future potential for bridging the dichotomies.

Historically, approaches to human-level intelligence have divided between those emphasizing the mechanisms involved, such as cognitive architectures, and those focusing on the knowledge content, such as ontologies. In this paper we argue that in order to build cognitive systems capable of human-level event-recognition, a comprehensive infrastructure of perceptual and cognitive mechanisms coupled with high-level knowledge representations is required. In particular, our contribution focuses on an integrated modeling framework (the “Cognitive Engine”), where the learning and knowledge retrieval mechanisms of the ACT-R cognitive architecture are combined with integrated semantic resources for the purpose of event interpretation.