We propose a simple model of network co-evolution in a game-dynamical system of interacting agents that play repeated games with their neighbors, and adapt their behaviors and network links based on the outcome of those games. The adaptation is achieved through a simple reinforcement learning scheme. We show that the collective evolution of such a system can be described by appropriately defined replicator dynamics equations. In particular, we suggest an appropriate factorization of the agents strategies thats results in a coupled system of equations characterizing the evolution of both strategies and network structure, and illustrate the framework on two simple examples.

Robustness and its many related concepts (stability, resilience, reliability, sustainability, etc.) are essential to understanding and maintaining systems of all kinds: engineered systems, ecologies, political regimes, computer algorithms, economies, homeostatic organisms, and decision procedures to name a few. However the concepts in this family have not been generally and formally defined and, as a result, the terms' uses across these various applications are inconsistent and sometimes contradictory. As part of a larger research project encompassing several categories of dynamical properties this paper distinguishes among several different robustness-related concepts using formal and general definitions of each. In addition to providing conceptual clarity through rigorous mathematical definitions, the techniques can also be used as domain-agnostic measures of the included properties. To help realize the potential of complex systems models we need such measures to capture features of processes that exhibit feedback, nonlinearity, heterogeneity, and emergence. The paper finishes with several branches of future work involving applications of these measures, more new measures for complex systems, and establishing of equivalence classes for the dynamics of complex systems for behavior-based categorization.

How do reasoning systems that learn evolve over time? Characterizing the evolution of these systems is important for understanding their limitations and gaining insights into the interplay between learning and reasoning. We describe an inverse ablation model for studying how learning and reasoning interact: Create a small knowledge base by ablation, and incrementally re-add facts, collecting snapshots of reasoning performance of the system to measure properties of interest. Experiments with this model suggest that different concepts show different rates of growth, and that the density of facts is an important parameter for modulating the rate of learning.

Collecting commonsense from text with the aid of a game can reduce the cost and effort of creating large knowledge bases. In this paper, we design, implement, and evaluate an online game that classifies, with input from players, text extracted from the Web as commonsense knowledge, domain-specific knowledge or nonsense. We also create a knowledge base that includes commonsense facts in natural language and information on how common a given fact is. The game is currently available for play on the Web and on Facebook, and under constant improvement. The creation of a continuous scale to classify commonsense helped during evaluation of the data by clearly identifying which knowledge is reliable and which needs further qualification. When comparing our results to other similar knowledge acquisition systems, our Turing Game performs better with respect to coverage,redundancy, and reliability of the commonsense acquired.

Games With A Purpose (or GWAP) provide an interesting way to collect data from web users. With over a million sentences collected and growing steadily, data verification becomes increasingly important. This research explores the alternative of designing human computation games specifically for verification purposes. Two games, Top10 and Pirate and Ghost, are designed for commonsense data verification. Top10 is a single-player game, in which the player attempts to guess the top answers to a given question. We use the frequency data to verify if the assertion is truly common. Pirate and Ghost is a multiplayer guessing role playing game in a network of concepts from the CSKB. We use the game data to identify the relation between two concepts. This paper presents the design of both games, and evaluate the efficiency and precision of each with two experiments. The results show that the two games can be coupled to achiever higher efficiency and precision in the data verification process.

When we are confronted with unexpected situations, we deal of background knowledge and special-purpose reasoners to with them by falling back on our general knowledge or making support general inference. Recent advances in text mining, analogies to other things we know. When software applications crowdsourcing, and professional knowledge engineering efforts fail, on the other hand, they often do so in brittle have finally led to commonsense knowledge bases of and unfriendly ways. At the same time, new application colleagues grappling with representation and reasoning, to domains are giving fresh insights into desiderata for common Doug Lenat, Push Singh, and Lenhart Schubert conducting sense reasoners and guidance for knowledge collection large scale engineering projects to construct collections efforts.

This paper describes Maryland Virtual Patient (MVP), a simulation and tutoring environment developed to support training cognitive decision making in clinical medicine. MVP is implemented as a society of agents, with one role – that of the trainee – played by a human and other roles played by artificial intelligent agents. In order to make the trainee’s experience as similar as possible to the traditional medical training environment, MVP is implemented as a collection of knowledge-based models of simulated human-like perception, reasoning and action processes. MVP operation involves metacognition: for example, the MVP virtual patient is aware of the physiological state of its body, of its physiological and character traits as well as of lacunae in its knowledge about the world and about language. This self-awareness influences the virtual patient’s reasoning and actions. In this paper we illustrate the role of metacognitive self-awareness in the overall operation of MVP.

In this paper we propose a middleware to reuse multimedia resources in order to produce new types of multimedia artifacts. In this work we adopt some basic concepts of film theory, such as the notions of plot, fabula and, in particular, diegetic time. The techniques we use are located within the area of artificial intelligence, using an explicit representation of time. The middleware consists of several modules, some devoted to the semantic annotation of multimedia components, and others to their visualization. Some modules regard the analysis of temporal connectivity and consistency of events. From a methodological point of view, an important module of the middleware contains the representation of a story (time of the narration and time of the story) and the temporal reasoning services, which are both implemented using a logic programming language (Flora2). Finally, there is a module in the middleware that translates the logical representation (in Flora2 language) into SMIL language, which allows the use of the final composition by a standard player.

In the last years, knowledge technologies have been exploited for self-regulation functionalities inside e-learning systems. The definition of integrated system suitably scaffolding learners to improve their experi- ence is still lacking though. In this work, we propose an innovative Web-based educational environment that sustains metacognitive self-regulated learning processes upon Semantic Web and Social Web methods and technologies.

Our learning by teaching environment has students take on the role and responsibilities of a teacher to a virtual student named Betty. The environment is structured so that successfully instructing their teachable agent requires the students to learn and understand science topics for themselves. This process is supported by adaptive scaffolding and feedback from the system. This feedback is instantiated through the interactions with the teachable agent and a mentor agent, named Mr. Davis. This paper provides an overview of two studies that were conducted with 5th grade science students and a description of the analysis techniques that we have developed for interpreting students’ activities in this learning environment.