School of Design

The development of more meaningful video games is becoming increasingly possible by recent advances in video game technologies, neurosciences, and biofeedback. In the near future, meaningful video games will be designed to facilitate personal-...

Fishgram, a novel algorithm for recognizing frequent or otherwise interesting sub-hypergraphs in large, heterogeneous hypergraphs, is presented. The algorithm's implementation the OpenCog integrative AGI framework is described, and concrete examples are given showing the patterns it recognizes in OpenCog's hypergraph knowledge store when the OpenCog system is used to control a virtual agent in a game world. It is argued that Fishgram is well suited to fill a critical niche in OpenCog and potentially other integrative AGI architectures: scalable recognition of relatively simple patterns in heterogeneous, potentially rapidly-changing data.

In order to explore the practical manifestations of the "cognitive synergy" between the PLN (Probabilistic Logic Networks) and ECAN (Economic Attention Network) components of the OpenCog AGI architecture, we explore the behavior of PLN and ECAN operating together on two standard test problems commonly used with Markov Logic Networks (MLN). Our preliminary results suggest that, while PLN can address these problems adequately, ECAN offers little added value for the problems in their standard form. However, we outline modified versions of the problem that we hypothesize would demonstrate the value of ECAN more effectively, via inclusion of confounding information that needs to be heuristically sifted through.

The OpenCogPrime cognitive architecture is founded on a principle of "cognitive synergy"-judicious combination of different cognitive algorithms, acting on different types of memory, in a way that helps overcome the combinatorial explosions each of the algorithms would suffer if used on its own. Here one manifestation of the cognitive synergy principle is explored-the use of probabilistic logical reasoning (based on declarative knowledge) to generalize procedural knowledge gained by evolutionary program learning. The use of this synergy is illustrated via an example drawn from a practical application of the OpenCog system to the analysis of gene expression data, wherein the MOSES program learning algorithm is used to recognize data patterns and the PLN inference engine is used to generalize these patterns via cross-referencing them with a biological ontology. This is a case study of both automated scientific inference, and synergetic cognitive processing.

General-purpose reasoning based on knowledge encoded in natural language, requires mapping this knowledge out of its syntax-dependent form into a more general representation that can be more flexibly applied and manipulated. We have created a system that accomplishes this in a variety of cases via mapping English syntactic expressions into predicate and term logic expressions, which can then be cognitively manipulated by tools such as a probabilistic logic engine, an information-theoretic pattern miner and others. Here we illustrate the functionality of this system in the particular case of comparative constructions.

An Application Programming Interface for human-level AGI systems is proposed, aimed at bridging the gap between proto-AGI R&D systems and practical AI application development. The API contains simply formalized queries corresponding to the various key aspects of human-like intelligence, organized so as to be independent of the algorithms used under the hood for query resolution and associated supporting cognitive processes. A novel, qualitative (and in principle quantifiable) measure of software general intelligence is proposed (the APIQ), measuring the degree to which a system succeeds at fulfilling the various API functions using a compact set of representations and algorithms.

The hypothesis is presented that "cognitive synergy"-proactive and mutually-assistive feedback between different cognitive processes associated with different types of memory-may serve as a foundation for advanced artificial general intelligence. A specific AI architecture founded on this idea, OpenCogPrime, is described, in the context of its application to control virtual agents and robots. The manifestations of cognitive synergy in OpenCogPrime's procedural and declarative learning algorithms are discussed in some detail.

Background: All eye movements are related in one way or another to our mental processes with lateral eye movements being associated with the different hemispheres of the brain. Eye movement techniques form the basis of eye movement desensitization and reprocessing therapy, wherein forced eye movements activate neurological pathways to treat the subject. Objective: The objective of our study was to examine the relationship between players' eye movements and their brain wave activities using a video game. Methods: We used similar eye movement techniques in the form of a video game called Lifeguard that could potentially stimulate different eye movement mode and create a more engaging experience for the user. By designing an experiment, we further explored the differences in electroencephalogram spectral power activity for the alpha, beta, theta, delta, and gamma frequency bands in Lifeguard and Tetris. Results: The game based on eye movement technologies resulted in decreased delta power and increased beta power, but significant difference between 2 games was not found. Conclusions: The applied uses of this research could mean that eye movement desensitization and reprocessing can be conducted in a more fun and engaging way through the use of gaming technology.

This study examines the relationship between player’s value systems and their actions in playing a massively multiplayer online role-playing game. Online survey data from 1,577 players were paired with their behavioral metrics within the game. A number of correlations were found between the scores of value system and the in-game metrics. Participants that scored high on the Red value system tend to spend more real money in the game, level up their character and ability as quickly as possible, and seek other achievements in the forms offered by game world. These characteristics for fun, power, and immediate gratification are also predicted by the Red value system. The finding provides valuable information on how to better design, evaluate, and understand enjoyment in games. The results also show the possibility of using the game as a platform in inferring players’ value systems and in training people to develop certain skills.

All mammals play, including humans. But there are undoubtedly differences between the types and forms of human and non-human animal play. Many of them are closely related to how humans externalize the maintenance and facilitation of highly complex game systems to technologies varying from mere pen and paper and mechanical tools to sophisticated digital, networked computer software. The range of human play seems to significantly exceed that of non-human animals and is meaningfully different in terms of technological advancement. It is therefore justifiable that most contemporary accounts of player-focused Game Studies assume a human player. In cultural theory of games we may take into account her cultural and ethnic background, social class and status or gender and sexual orientation, but ultimately she is one of us and alike the subjects studying her: a human being. However, theories of Game Studies afford approaching humans and non-human animals equals as players and forms an advan...

A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by transformer neural network language models to guide symbolic learning processes like clustering and rule induction. This method exploits the learned linguistic knowledge in transformers, without any reference to their inner representations; hence, the technique is readily adaptable to the continuous appearance of more powerful language models. We show a proof-of-concept example of our proposed technique, using it to guide unsupervised symbolic link-grammar induction methods drawn from our prior research.