Bradly Alicea

Bradly Alicea, Jesse Parent

What role does phenotypic complexity play in the systems-level function of an embodied agent? The organismal phenotype is a topologically complex structure that interacts with a genotype, developmental physics, and an informational environment. Using this observation as inspiration, we utilize a type of embodied agent that exhibits layered representational capacity: meta-brain models. Meta-brains are used to demonstrate how phenotypes process information and exhibit self-regulation from development to maturity. We focus on two candidate structures that potentially explain this capacity: folding and layering. As layering and folding can be observed in a host of biological contexts, they form the basis for our representational investigations. First, an innate starting point (genomic encoding) is described. The generative output of this encoding is a differentiation tree, which results in a layered phenotypic representation. Then we specify a formal meta-brain model of the gut, which exhibits folding and layering in development in addition to different degrees of representation of processed information. This organ topology is retained in maturity, with the potential for additional folding and representational drift in response to inflammation. Next, we consider topological remapping using the developmental Braitenberg Vehicle (dBV) as a toy model. During topological remapping, it is shown that folding of a layered neural network can introduce a number of distortions to the original model, some with functional implications. The paper concludes with a discussion on how the meta-brains method can assist us in the investigation of enactivism, holism, and cognitive processing in the context of biological simulation.

Bradly Alicea, Jesse Parent

Artificial Intelligence (AI) systems based solely on neural networks or symbolic computation present a representational complexity challenge. While minimal representations can produce behavioral outputs like locomotion or simple decision-making, more elaborate internal representations might offer a richer variety of behaviors. We propose that these issues can be addressed with a computational approach we call meta-brain models. Meta-brain models are embodied hybrid models that include layered components featuring varying degrees of representational complexity. We will propose combinations of layers composed using specialized types of models. Rather than using a generic black box approach to unify each component, this relationship mimics systems like the neocortical-thalamic system relationship of the mammalian brain, which utilizes both feedforward and feedback connectivity to facilitate functional communication. Importantly, the relationship between layers can be made anatomically explicit. This allows for structural specificity that can be incorporated into the model's function in interesting ways. We will propose several types of layers that might be functionally integrated into agents that perform unique types of tasks, from agents that simultaneously perform morphogenesis and perception, to agents that undergo morphogenesis and the acquisition of conceptual representations simultaneously. Our approach to meta-brain models involves creating models with different degrees of representational complexity, creating a layered meta-architecture that mimics the structural and functional heterogeneity of biological brains, and an input/output methodology flexible enough to accommodate cognitive functions, social interactions, and adaptive behaviors more generally. We will conclude by proposing next steps in the development of this flexible and open-source approach.

Ankit Grover, Vaishali Yadav, Bradly Alicea

One important feature of complex systems are problem domains that have many local minima and substructure. Biological systems manage these local minima by switching between different subsystems depending on their environmental or developmental context. Genetic Algorithms (GA) can mimic this switching property as well as provide a means to overcome problem domain complexity. However, standard GA requires additional operators that will allow for large-scale exploration in a stochastic manner. Gradient-free heuristic search techniques are suitable for providing an optimal solution in the discrete domain to such single objective optimization tasks, particularly compared to gradient-based methods which are noticeably slower. To do this, the authors turn to an optimization problem from the flight scheduling domain. The authors compare the performance of such common gradient-free heuristic search algorithms and propose variants of GAs. The Iterated Chaining (IC) method is also introduced, building upon traditional chaining techniques by triggering multiple local searches instead of the singular action of a mutation operator. The authors will show that the use of multiple local searches can improve performance on local stochastic searches, providing ample opportunity for application to a host of other problem domains. It is observed that the proposed GA variants have the least average cost across all benchmarks including the problem proposed and IC algorithm performs better than its constituents.

Hrishikesh Kulkarni, Bradly Alicea

Literary artefacts are generally indexed and searched based on titles, meta data and keywords over the years. This searching and indexing works well when user/reader already knows about that particular creative textual artefact or document. This indexing and search hardly takes into account interest and emotional makeup of readers and its mapping to books. When a person is looking for a literary textual artefact, he/she might be looking for not only information but also to seek the joy of reading. In case of literary artefacts, progression of emotions across the key events could prove to be the key for indexing and searching. In this paper, we establish clusters among literary artefacts based on computational relationships among sentiment progressions using intelligent text analysis. We have created a database of 1076 English titles + 20 Marathi titles and also used database http://www.cs.cmu.edu/~dbamman/booksummaries.html with 16559 titles and their summaries. We have proposed Sentiment Progression based Indexing for searching and recommending books. This can be used to create personalized clusters of book titles of interest to readers. The analysis clearly suggests better searching and indexing when we are targeting book lovers looking for a particular type of book or creative artefact. This indexing and searching can find many real-life applications for recommending books.

Krishna Katyal, Jesse Parent, Bradly Alicea

While Artificial Neural Networks (ANNs) have yielded impressive results in the realm of simulated intelligent behavior, it is important to remember that they are but sparse approximations of Biological Neural Networks (BNNs). We go beyond comparison of ANNs and BNNs to introduce principles from BNNs that might guide the further development of ANNs as embodied neural models. These principles include representational complexity, complex network structure/energetics, and robust function. We then consider these principles in ways that might be implemented in the future development of ANNs. In conclusion, we consider the utility of this comparison, particularly in terms of building more robust and dynamic ANNs. This even includes constructing a morphology and sensory apparatus to create an embodied ANN, which when complemented with the organizational and functional advantages of BNNs unlocks the adaptive potential of lifelike networks.

Bradly Alicea, Rishabh Chakrabarty, Stefan Dvoretskii et al.

There is much to learn through synthesis of Developmental Biology, Cognitive Science and Computational Modeling. Our path forward involves a design for developmentally-inspired learning agents based on Braitenberg Vehicles. Continual developmental neurosimulation allows us to consider the role of developmental trajectories in bridging the related phenomena of nervous system morphogenesis, developmental learning, and plasticity. Being closely tied to continual learning, our approach is tightly integrated with developmental embodiment, and can be implemented using a type of agent called developmental Braitenberg Vehicles (dBVs). dBVs begin their lives as a set of undefined structures that transform into agent-based systems including a body, sensors, effectors, and nervous system. This phenotype is characterized in terms of developmental timing: with distinct morphogenetic, critical, and acquisition (developmental learning) periods. We further propose that network morphogenesis can be accomplished using a genetic algorithmic approach, while developmental learning can be implemented using a number of computational methodologies. This approach provides a framework for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neuronal networks and active learning on these networks. In conclusion, we will consider agent learning and development at different timescales, from very short (<100ms) intervals to long-term evolution. The development, evolution, and learning in an embodied agent-based approach is key to an integrative view of biologically-inspired intelligence.

Hrishikesh Kulkarni, Bradly Alicea

Culture is core to human civilization, and is essential for human intellectual achievements in social context. Culture also influences how humans work together, perform particular task and overall lifestyle and dealing with other groups of civilization. Thus, culture is concerned with establishing shared ideas, particularly those playing a key role in success. Does it impact on how two individuals can work together in achieving certain goals? In this paper, we establish a means to derive cultural association and map it to culturally mediated success. Human interactions with the environment are typically in the form of expressions. Association between culture and behavior produce similar beliefs which lead to common principles and actions, while cultural similarity as a set of common expressions and responses. To measure cultural association among different candidates, we propose the use of a Graphical Association Method (GAM). The behaviors of candidates are captured through series of expressions and represented in the graphical form. The association among corresponding node and core nodes is used for the same. Our approach provides a number of interesting results and promising avenues for future applications.

Bradly Alicea

Human Augmentation (HA) spans several technical fields and methodological approaches, including Experimental Psychology, Human-Computer Interaction, Psychophysiology, and Artificial Intelligence. Augmentation involves various strategies for optimizing and controlling cognitive states, which requires an understanding of biological plasticity, dynamic cognitive processes, and models of adaptive systems. As an instructive lesson, we will explore a few HA-related concepts and outstanding issues. Next, we focus on inducing and controlling HA using experimental methods by introducing three techniques for HA implementation: learning augmentation, augmentation using physical media, and extended phenotype modeling. To conclude, we will review integrative approaches to augmentation, which transcend specific functions.

Bradly Alicea

A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed.