4 research outputs found

    Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero

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    Artificial Intelligence (AI) systems have made remarkable progress, attaining super-human performance across various domains. This presents us with an opportunity to further human knowledge and improve human expert performance by leveraging the hidden knowledge encoded within these highly performant AI systems. Yet, this knowledge is often hard to extract, and may be hard to understand or learn from. Here, we show that this is possible by proposing a new method that allows us to extract new chess concepts in AlphaZero, an AI system that mastered the game of chess via self-play without human supervision. Our analysis indicates that AlphaZero may encode knowledge that extends beyond the existing human knowledge, but knowledge that is ultimately not beyond human grasp, and can be successfully learned from. In a human study, we show that these concepts are learnable by top human experts, as four top chess grandmasters show improvements in solving the presented concept prototype positions. This marks an important first milestone in advancing the frontier of human knowledge by leveraging AI; a development that could bear profound implications and help us shape how we interact with AI systems across many AI applications.Comment: 61 pages, 29 figure

    Collaborative Game-based Learning - Automatized Adaptation Mechanics for Game-based Collaborative Learning using Game Mastering Concepts

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    Learning and playing represent two core aspects of the information and communication society nowadays. Both issues are subsumed in Digital Education Games, one major field of Serious Games. Serious Games combine concepts of gaming with a broad range of application fields: among others, educational sectors and training or health and sports, but also marketing, advertisement, political education, and other societally relevant areas such as climate, energy, and safety. This work focuses on collaborative learning games, which are Digital Educational Games that combine concepts from collaborative learning with game concepts and technology. Although Digital Educational Games represent a promising addition to existing learning and teaching methods, there are different challenges opposing their application. The tension between a game that is supposed to be fun and the facilitation of serious content constitutes a central challenge to game design. The often high technical complexity and especially the instructors' lack of control over the game represent further challenges. Beyond that, the distinct heterogeneity of learners who often have different play styles, states of knowledge, learning speed, and soft skills, such as teamwork or communication skills, forms a pivotal problem. Apart from that, the vital role of the instructor needs to be taken into account. Within the scope of this dissertation, the problems mentioned above are analyzed, concepts to solve them introduced, and methods developed to address them. The first major contribution contains the conceptualization of a framework for adaptation of collaborative multiplayer games as well as for the control of those games at run-time through an instructor using the Game Master principle. The core concept hereby addresses the design of a model to represent heterogeneous groups and to represent collaborative Serious Games. Based on that, a novel concept for adaptation of collaborative multiplayer games is developed, implemented, and evaluated. Automatic recognition and interpretation of game situations, as well as determination of the most well suited adaptation based on the recognized situations, is a major challenge here. Further, a concept is developed to integrate an instructor in a meaningful way into the course of the game, giving him/her the necessary resources to recognize problems and to intervene and adapt the game at run-time. Therefore, it will be taken into account that the elaborated concepts are applicable in a generic way independent of the underlying game. The second major contribution of this work is the conceptualization and design of a simulation of players and learners in a collaborative multiplayer game that behave realistically based on a player, learner, and interaction model. This is supposed to enable an evaluation of the adaptation and Game Mastering concepts using freely configurable player and learner types. The concepts introduced and developed within this thesis have been thoroughly evaluated using a twofold approach. As a test environment, a collaborative multiplayer Serious Game was designed and implemented. Within that simulation environment, the developed Game Mastering and adaptation concepts were assessed and tested with large sets of virtual learners. Additionally, the concepts were evaluated with real users. Therefore, two different evaluation studies with a total of 60 participants were conducted. The results of the conducted evaluations help to broaden the areas of application of Serious Games as well as to improve their applicability, hence raising acceptance among instructors. The models, architectures, and software solutions developed within this thesis thus build a foundation for further research of multiplayer Serious Games

    Use-driven concept formation

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-165).When faced with a complex task, humans often identify domain-specific concepts that make the task more tractable. In this thesis, I investigate the formation of domain-specific concepts of this sort. I propose a set of principles for formulating domain-specific concepts, including a new inductive bias that I call the equivalence class principle. I then use the domain of two-player, perfect-information games to test and refine those principles. I show how the principles can be applied in a semiautomated fashion to identify strategically-important visual concepts, discover highlevel structure in a game's state space, create human-interpretable descriptions of tactics, and uncover both offensive and defensive strategies within five deterministic, perfect-information games that have up to forty-two million states apiece. I introduce a visualization technique for networks that discovers a new strategy for exploiting an opponent's mistakes in lose tic-tac-toe; discovers the optimal defensive strategies in five and six men's morris; discovers the optimal offensive strategies in pong hau k'i, tic-tac-toe, and lose tic-tac-toe; simplifies state spaces by up to two orders of magnitude; and creates a hierarchical depiction of a game's state space that allows the user to explore the space at multiple levels of granularity. I also introduce the equivalence class principle, an inductive bias that identifies concepts by building connections between two representations in the same domain. I demonstrate how this principle can be used to rediscover visual concepts that would help a person learn to play a game, propose a procedure for using such concepts to create succinct, human-interpretable descriptions of offensive and defensive tactics, and show that these tactics can compress important information in the five men's morris state space by two orders of magnitude.by Jennifer M. Roberts.Ph.D

    Stochastic Reasoning with Action Probabilistic Logic Programs

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    In the real world, there is a constant need to reason about the behavior of various entities. A soccer goalie could benefit from information available about past penalty kicks by the same player facing him now. National security experts could benefit from the ability to reason about behaviors of terror groups. By applying behavioral models, an organization may get a better understanding about how best to target their efforts and achieve their goals. In this thesis, we propose action probabilistic logic (or ap-) programs, a formalism designed for reasoning about the probability of events whose inter-dependencies are unknown. We investigate how to use ap-programs to reason in the kinds of scenarios described above. Our approach is based on probabilistic logic programming, a well known formalism for reasoning under uncertainty, which has been shown to be highly flexible since it allows imprecise probabilities to be specified in the form of intervals that convey the inherent uncertainty in the knowledge. Furthermore, no independence assumptions are made, in contrast to many of the probabilistic reasoning formalisms that have been proposed. Up to now, all work in probabilistic logic programming has focused on the problem of entailment, i.e., verifying if a given formula follows from the available knowledge. In this thesis, we argue that other problems also need to be solved for this kind of reasoning. The three main problems we address are: Computing most probable worlds: what is the most likely set of actions given the current state of affairs?; answering abductive queries: how can we effect changes in the environment in order to evoke certain desired actions?; and Reasoning about promises: given the importance of promises and how they are fulfilled, how can we incorporate quantitative knowledge about promise fulfillment in ap-programs? We address different variants of these problems, propose exact and heuristic algorithms to scalably solve them, present empirical evaluations of their performance, and discuss their application in real world scenarios
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