Ali A. Minai

Cooperative real-time search and task allocation in UAV teams

In this paper, we consider a heterogeneous team of UAVs drawn from several distinct classes and engaged in a search and destroy mission over an extended battlefield. Several different types of targets are considered. Some target locations are suspected a priori with a certain probability, while the others are initially unknown. During the mission, the UAVs perform Search, Confirm, Attack and Battle Damage Assessment (BDA) tasks at various locations. The target locations are detected gradually through search, while the tasks are determined in real-time by the actions of all UAVs and their results (e.g., sensor readings), which makes the task dynamics stochastic. The tasks must, therefore, be allocated to UAVs in real-time as they arise. Each class of UAVs has its own sensing and attack capabilities with respect to the different target types, so the need for appropriate and efficient assignment is paramount. We present a simple cooperative approach to this problem, based on distributed assignment mediated through centralized mission status information. Using this information, each UAV assesses the task opportunities available to it, and makes commitments through a phased incremental process. This produces a simple, flexible, scalable and inherently decentralizable method for task allocation. Concurrently, every UAV also monitors the degree to which various parts of the environment have been searched, and accommodates this information in planning its paths. We study the effect of various decision parameters, target distributions, and UAV team characteristics on the performance of our approach.

Efficient associative memory using small-world architecture

Abstract Most models of neural associative memory have used networks with broad connectivity. However, from both a neurobiological viewpoint and an implementation perspective, it is logical to minimize the length of inter-neural connections and consider networks whose connectivity is predominantly local. The “small-world networks” model described recently by Watts and Strogatz provides an interesting approach to this issue. In this paper, we show that associative memory networks with small-world architectures can provide the same retrieval performance as randomly connected networks while using a fraction of the total connection length.

Balancing search and target response in cooperative unmanned aerial vehicle (UAV) teams

This paper considers a heterogeneous team of cooperating unmanned aerial vehicles (UAVs) drawn from several distinct classes and engaged in a search and action mission over a spatially extended battlefield with targets of several types. During the mission, the UAVs seek to confirm and verifiably destroy suspected targets and discover, confirm, and verifiably destroy unknown targets. The locations of some (or all) targets are unknown a priori, requiring them to be located using cooperative search. In addition, the tasks to be performed at each target location by the team of cooperative UAVs need to be coordinated. The tasks must, therefore, be allocated to UAVs in real time as they arise, while ensuring that appropriate vehicles are assigned to each task. Each class of UAVs has its own sensing and attack capabilities, so the need for appropriate assignment is paramount. In this paper, an extensive dynamic model that captures the stochastic nature of the cooperative search and task assignment problems is developed, and algorithms for achieving a high level of performance are designed. The paper focuses on investigating the value of predictive task assignment as a function of the number of unknown targets and number of UAVs. In particular, it is shown that there is a tradeoff between search and task response in the context of prediction. Based on the results, a hybrid algorithm for switching the use of prediction is proposed, which balances the search and task response. The performance of the proposed algorithms is evaluated through Monte Carlo simulations.

Back-propagation heuristics: a study of the extended delta-bar-delta algorithm

An investigation is presented of an extension, proposed by A.A. Minai and R.D. Williams (Proc. Int. Joint Conf. on Neural Networks, vol.1, p.676-79, Washington, DC, 1990), to an algorithm for training neural networks in real-valued, continuous approximation domains. Specifically, the most effective aspects of the proposed extension are isolated. It is found that while momentum is particularly useful for the delta-bar-delta algorithm, it cannot be used conveniently because of sensitivity considerations. It is also demonstrated that by using more subtle versions of the algorithm, the advantages of momentum can be retained without any significant drawbacks

Investigating the predictability of essential genes across distantly related organisms using an integrative approach

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.

Latent Attractors: A Model for Context-Dependent Place Representations in the Hippocampus

Cells throughout the rodent hippocampal system show place-specific patterns of firing called place fields, creating a coarse-coded representation of location. The dependencies of this place codeor cognitive mapon sensory cues have been investigated extensively, and several computational models have been developed to explain them. However, place representations also exhibit strong dependence on spatial and behavioral context, and identical sensory environments can produce very different place codes in different situations. Several recent studies have proposed models for the computational basis of this phenomenon, but it is still not completely understood. In this article, we present a very simple connectionist model for producing context-dependent place representations in the hippocampus. We propose that context dependence arises in the den-tate gyrus-hilus (DGH) system, which functions as a dynamic selector, disposing a small group of granule and pyramidal cells to fire in response to afferent stimulus while depressing the rest. It is hypothesized that the DGH system dynamics has latent attractors, which are unmasked by the afferent input and channel system activity into subpopulations of cells in the DG, CA3, and other hippocampal regions as observed experimentally. The proposed model shows that a minimally structured hippocampus-like system can robustly produce context-dependent place codes with realistic attributes.

The dynamics of sparse random networks

Recurrent neural networks with full symmetric connectivity have been extensively studied as associative memories and pattern recognition devices. However, there is considerable evidence that sparse, asymmetrically connected, mainly excitatory networks with broadly directed inhibition are more consistent with biological reality. In this paper, we use the technique of return maps to study the dynamics of random networks with sparse, asymmetric connectivity and nonspecific inhibition. These networks show three qualitatively different kinds of behavior: fixed points, cycles of low period, and extremely long cycles verging on aperiodicity. Using statistical arguments, we relate these behaviors to network parameters and present empirical evidence for the accuracy of this statistical model. The model, in turn, leads to methods for controlling the level of activity in networks. Studying random, untrained networks provides an understanding of the intrinsic dynamics of these systems. Such dynamics could provide a substrate for the much more complex behavior shown when synaptic modification is allowed.

2009 Special Issue: Neural dynamics of idea generation and the effects of priming

Idea generation is a fundamental attribute of the human mind, but the cognitive and neural mechanisms underlying this process remain unclear. In this paper, we present a dynamic connectionist model for the generation of ideas within a brainstorming context. The key hypothesis underlying the model is that ideas emerge naturally from itinerant attractor dynamics in a multi-level, modular semantic space, and the potential surface underlying this dynamics is itself shaped dynamically by task context, ongoing evaluative feedback, inhibitory modulation, and short-term synaptic modification. While abstract, the model attempts to capture the interplay between semantic representations, working memory, attentional selection, reinforcement signals, and modulation. We show that, once trained on a set of contexts and ideas, the system can rapidly recall stored ideas in familiar contexts, and can generate novel ideas by efficient, multi-level dynamical search in both familiar and unfamiliar contexts. We also use a simplified continuous-time instantiation of the model to explore the effect of priming on idea generation. In particular, we consider how priming low-accessible categories in a connectionist semantic network can lead to the generation of novel ideas. The mapping of the model onto various regions and modulatory processes in the brain is also discussed briefly.

Setting the activity level in sparse random networks

We investigate the dynamics of a class of recurrent random networks with sparse, asymmetric excitatory connectivity and global shunting inhibition mediated by a single interneuron. Using probabilistic arguments and a hyperbolic tangent approximation to the gaussian, we develop a simple method for setting the average level of firing activity in these networks. We demonstrate through simulations that our technique works well and extends to networks with more complicated inhibitory schemes. We are interested primarily in the CA3 region of the mammalian hippocampus, and the random networks investigated here are seen as modeling the a priori dynamics of activity in this region. In the presence of external stimuli, a suitable synaptic modification rule could shape this dynamics to perform temporal information processing tasks such as sequence completion and prediction.

Evidential map-building approaches for multi-UAV cooperative search

This paper addresses the map building problem for cooperative search by a team of uninhabited air vehicles (UAVs) operating in an unknown and uncertain environment . We present and compare two evidential map-building approaches based on Bayesian theory and Dempster-Shafer theory respectively. We illustrate how to utilize the generated maps into the UAVs path planning procedure so that they could cooperatively localize targets in the environment. The simulation results illustrate the effectiveness of the proposed strategies.