120,320 research outputs found
Level-of-detail for cognitive real-time characters
We present a solution for the real-time simulation of artificial environments containing cognitive and hierarchically organized agents at constant rendering framerates. We introduce a level-of-detail concept to behavioral modeling, where agents populating the world can be both reactive and proactive. The disposable time per rendered frame for behavioral simulation is variable and determines the complexity of the presented behavior. A special scheduling algorithm distributes this time to the agents depending on their level-of-detail such that visible and nearby agents get more time than invisible or distant agents. This allows for smooth transitions between reactive and proactive behavior. The time available per agent influences the proactive behavior, which becomes more sophisticated because it can spend time anticipating future situations. Additionally, we exploit the use of hierarchies within groups of agents that allow for different levels of control. We show that our approach is well-suited for simulating environments with up to several hundred agents with reasonable response times and the behavior adapts to the current viewpoin
English for Students of Philology (Англійська мова для студентів-філологів)
Методичні рекомендації містять матеріал, необхідний для проведення практичних занять та організації самостійної роботи з англійської мови студентів-магістрантів ННІ філології та журналістики. Тексти, вправи, тести та рекомендації методичного характеру подані у послідовності, окресленої Програмою (затвердженою у 2013 році), для виконання чотирьох основних змістовних модулів. Матеріал розрахований на поглиблення фахових спеціальних та загальних комунікативних навичок студентів у процесі професійно спрямованого вивчення англійської мови.
Для денної та заочної форм навчання
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
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Recognition by directed attention to recursively partitioned images
A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth
Agents for educational games and simulations
This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications
Information content versus word length in random typing
Recently, it has been claimed that a linear relationship between a measure of
information content and word length is expected from word length optimization
and it has been shown that this linearity is supported by a strong correlation
between information content and word length in many languages (Piantadosi et
al. 2011, PNAS 108, 3825-3826). Here, we study in detail some connections
between this measure and standard information theory. The relationship between
the measure and word length is studied for the popular random typing process
where a text is constructed by pressing keys at random from a keyboard
containing letters and a space behaving as a word delimiter. Although this
random process does not optimize word lengths according to information content,
it exhibits a linear relationship between information content and word length.
The exact slope and intercept are presented for three major variants of the
random typing process. A strong correlation between information content and
word length can simply arise from the units making a word (e.g., letters) and
not necessarily from the interplay between a word and its context as proposed
by Piantadosi et al. In itself, the linear relation does not entail the results
of any optimization process
Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs
Conversational participants tend to immediately and unconsciously adapt to
each other's language styles: a speaker will even adjust the number of articles
and other function words in their next utterance in response to the number in
their partner's immediately preceding utterance. This striking level of
coordination is thought to have arisen as a way to achieve social goals, such
as gaining approval or emphasizing difference in status. But has the adaptation
mechanism become so deeply embedded in the language-generation process as to
become a reflex? We argue that fictional dialogs offer a way to study this
question, since authors create the conversations but don't receive the social
benefits (rather, the imagined characters do). Indeed, we find significant
coordination across many families of function words in our large movie-script
corpus. We also report suggestive preliminary findings on the effects of gender
and other features; e.g., surprisingly, for articles, on average, characters
adapt more to females than to males.Comment: data available at http://www.cs.cornell.edu/~cristian/movie
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The challenges of viewpoint-taking when learning a sign language: Data from the 'frog story' in British Sign Language
Little is known about how hearing adults learn sign languages. Our objective in this study was to investigate how learners of British Sign Language (BSL) produce narratives, and we focused in particular on viewpoint-taking. Twenty-three intermediate-level learners of BSL and 10 deaf native/early signers produced a narrative in BSL using the wordless picture book Frog, where are you? (Mayer, 1969). We selected specific episodes from part of the book that provided rich opportunities for shifting between different characters and taking on different viewpoints. We coded for details of story content, the frequency with which different viewpoints were used and how long those viewpoints were used for, and the numbers of articulators that were used simultaneously. We found that even though learners’ and deaf signers’ narratives did not differ in overall duration, learners’ narratives had less content. Learners used character viewpoint less frequently than deaf signers. Although learners spent just as long as deaf signers in character viewpoint, they spent longer than deaf signers in observer viewpoint. Together, these findings suggest that character viewpoint was harder than observer viewpoint for learners. Furthermore, learners were less skilled than deaf signers in using multiple articulators simultaneously. We conclude that challenges for learners of sign include taking character viewpoint when narrating a story and encoding information across multiple articulators simultaneously
Morality Play: A Model for Developing Games of Moral Expertise
According to cognitive psychologists, moral decision-making is a dual-process
phenomenon involving two types of cognitive processes: explicit reasoning and
implicit intuition. Moral development involves training and integrating both types of
cognitive processes through a mix of instruction, practice, and reflection. Serious
games are an ideal platform for this kind of moral training, as they provide safe spaces
for exploring difficult moral problems and practicing the skills necessary to resolve
them. In this article, we present Morality Play, a model for the design of serious games
for ethical expertise development based on the Integrative Ethical Education framework
from moral psychology and the Lens of the Toy model for serious game design
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