Automated question generation has been explored for a broad range of tasks. However, an important task for which limited work on question generation has been undertaken is writing support. Writing support systems, particularly for novice writers who are acquiring the fundamentals of writing, can scaffold the complex processes that bear on writing. Novice writers face significant challenges in creative writing. Their stories often lack the expressive prose that characterizes texts produced by their expert writer counterparts. A story that is composed by a novice writer may also lack a compelling plot, may not effectively utilize a story’s setting, characters, and props, and may describe events that play out in an unpredictable or confusing order. We propose an automatic question generation framework that is designed to stimulate the cognitive processes associated with creative writing. The framework utilizes semantic role labeling and discourse parsing applied to the initial drafts of the writer’s passage to generate questions to promote creativity.

Natural language is a uniquely convenient means of communication due to, among its other properties, its flexibility and its openness to interpretation. These properties of natural language are largely made possible by its heavy dependence on context and common ground. Drawing on elements of Clark’s account of language use, we view natural language interactions as a coordination problem involving agents who work together to convey and thus coordinate their interaction goals. In the modeling work presented here, a sequence of interrelated modules developed in the Polyscheme cognitive architecture is used to implement several stages of reasoning the user of a simple video application would expect an addressee—ultimately, the application—to work through, if the interaction goal was to locate a scene they had previously viewed together.

One challenge for cognitive architectures is to effectively use different forms of knowledge and learning. We present a case study of Soar agents that play a multiplayer dice game, in which probabilistic reasoning and heuristic symbolic knowledge appear to play a central role. We develop and evaluate a collection of agents that use different combinations of probabilistic decision making, heuristic symbolic reasoning, opponent modeling, and learning. We demonstrate agents that use Soar’s rule learning mechanism (chunking) to convert deliberate reasoning with probabilities into implicit reasoning, and then use reinforcement learning to further tune performance.

The better the team mental model, the better the teamwork. An important aspect of what determines a good team model is the extent to which the model is shared by the team members. This paper presents suggestions for measuring the extent to which teams have a shared mental model and describes how these measures are related to team performance. The most promising measures of sharedness proposed so far rely on using a compositional approach for team modeling and on a situation-sensitive relevance relation that indicates to what extent components contribute to team performance. A case study illustrates the approach and initial results on measuring performance when teams use different levels of sharedness.

This paper explores the application of computer simulation techniques to the fields of literary studies and narratology by developing a model for plot structure and characterization. Using a corpus of 19th Century British novels as a case study, the author begins with a descriptive quantitative analysis of character names, developing a set of stylized facts about the way narratives allocate attention to their characters. The author shows that narrative attention in many novels appears to follow a “long tail” distribution.The author then constructs an explanatory model in NetLogo, demonstrating that basic assumptions about plot structure are sufficient to generate output consistent with the real novels in the corpus.

Organizations play a pivotal role in the dynamics of social, economic, and ecological systems. Current organizational life-cycle models do not adequately consider the impact of propensities (deeply ingrained preferences and patterns of behavior) on organizational culture and evolution. On a global basis, the predominant thinking modes in organizations are driven by senior executives, marketers, financial experts, legal resources, and the engineers and scientists that create our technology-rich world. Each of these groups has, in aggregate, embedded propensities or tendencies that profoundly shape decision-making patterns and overall social dynamics. Dominant propensities can make organizations vulnerable to risks by inhibiting the level of systems thinking and networking necessary to ensure integration within a global socio-ecological context. The spectrum of propensities within an organization shapes the relative resilience of its human and management systems, and ultimately determines organizational effectiveness. This paper proposes a model for organizational evolution that links the role of propensities to adaptability and resilience. Conscious effort to expand the intelligence of organizations through diversification of propensities better equips organizations to achieve adaptability and sustainability.

This paper is about generating questions in human-robot interaction. We survey existing work on the forms and meanings of questions in English and discuss the pragmatic effects resulting from an interplay between the choice of syntactic form and intonation. We propose an approach to formalization based on a notion of common ground and commitment, set in a model of situated dialogue as part of collaborative activity where we explicitly model the beliefs and intentions of both the robot and the human. Questions come about by abductively inferring an intentional structure grounded in the belief model and indicating commitments. Content planning and surface realization turn this into a question of the appropriate form.

This paper discusses the challenge of designing instructable agents that can learn through interaction with a human expert. Learning through instruction is a powerful paradigm for acquiring knowledge because it limits the complexity of the learning task in a variety of ways. To support learning through instruction, the agent must be able to effectively communicate its lack of knowledge to the human, comprehend instructions, and apply them to the ongoing task. Weidentify some problems of concern when designing instructable agents. We propose an agent design that addresses some of these problems. We instantiate this design in the Soar cognitive architecture and analyze its capabilities on a learning task.

This paper studies 3-state, 2-direction Mobile Au- tomata. The results of this study show that although it is more difficult to find complexity in Mobile Automata than Cellular Automata, 3-color Mobile Automata can still be divided into four classes of complexity, thus pro- ducing complex behavior. There are 627 number of 3- color Mobile Automata, which were studied and filtered to prove the complexity of Mobile automata. The results of this study infer that it is possible to observe complex- ity in systems that contain only one active cell, if the system has more then two states.

This article presents a method for using outcome matrices for social phrase selection. An outcome matrix is a computational representation of interaction often used to represent a social decision problem. Typically an outcome matrix lists the potential actions that a robot or agent might select and how the selection of each possible action will impact both the agent and their interactive partner. Here we examine the possibility of replacing the social actions listed in a matrix with phrases that could be spoken by the robot. We show that doing so allows one to utilize several tools from interdependence theory and game theory.