Evolving Error Tolerance in Biologically-Inspired iAnt Robots

Hecker, Joshua P.; Stolleis, Karl; Swenson, Bjorn; Letendre, Kenneth; Moses, Melanie E.

doi:10.7551/978-0-262-31709-2-ch153

Cited by 8 publications

(10 citation statements)

References 27 publications

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“…We identify the type of search used by T cells, then apply the observed three-dimensional search characteristics to a simple continuous space model. We simulate and characterize the performance of T cell inspired search strategies in robots using the iAnt robot system (Hecker et al, 2013). We found heavy-tailed search to be so effective for our simulated iAnts that we have begun incorporating it into the current multi-robot foraging algorithm.…”

Section: Biological Contextmentioning

confidence: 99%

From Microbiology to Microcontrollers: Robot Search Patterns Inspired by T Cell Movement

Fricke¹,

Asperti-Boursin

Hecker

et al. 2013

Advances in Artificial Life, ECAL 2013

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In order to trigger an adaptive immune response, T cells move through lymph nodes searching for dendritic cells that carry antigens indicative of infection. We observe T cell movement in lymph nodes and implement those movement patterns as a search strategy in a team of simulated robots. We find that the distribution of step-sizes taken by T cells are best described by heavy-tailed (Lévy-like) distributions. Such distributions are characterized by many small steps and rare large steps. Our simulations show that heavy-tailed motion leads to dramatically faster search compared to Brownian motion, both in groups of T cells and in teams of robots. The mechanisms that cause heavy-tailed movement patterns in T cells are not fully understood. However, in robot simulations we find that heavy-tailed movement improves search speed whether that movement is caused by rules intrinsic to the robots or by adaptive response to extrinsic factors in the environment.

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Section: Biological Contextmentioning

confidence: 99%

From Microbiology to Microcontrollers: Robot Search Patterns Inspired by T Cell Movement

Fricke¹,

Asperti-Boursin

Hecker

et al. 2013

Advances in Artificial Life, ECAL 2013

Self Cite

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“…One advantage of this approach over the current iAnt model described by Hecker at al. [10] (which increases turning angles of successive steps to achieve more thorough search) is that a Lévy-like walk might be tuned with fewer parameters. Additionally, there is substantial mathematical analysis of Lévy walk efficiency that may allow iAnts to tune their search based on analytical predictions of the most efficient walk for a given environment, without needing to evolve.…”

Section: Discussionmentioning

confidence: 99%

“…A genetic algorithm (GA) evolves parameters that control the sensitivity threshold for triggering behaviours, the likelihood of transitioning from one behaviour to another, and the length of time each behaviour should last. In this work we analyse data from a simulation, which is carefully parametrised to reflect the behaviour, sensing, and navigation error of physical iAnt robots [10].…”

Section: Iant Robot Swarmsmentioning

confidence: 99%

Distinguishing Adaptive Search from Random Search in Robots and T cells

Fricke¹,

Black

Hecker

et al. 2015

Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

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In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the LN environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently than a random model of search would suggest. We then test whether T cells that visit such locations have different movement patterns than other T cells. Our analysis suggests that T cells do adapt their movement in response to cues that may indicate the locations of DC targets. We test the ability of our method to identify frequently visited sites in T cells and in a swarm of simulated iAnt robots evolved to search using a suite of biologically-inspired behaviours. We compare the movement of T cells and robots that repeatedly sample the same locations in space with the movement of agents that do not resample space in order to understand whether repeated sampling alters movement. Our analysis suggests that specific environmental cues can be inferred from the movement of T cells. While the precise identity of these cues remains unknown, comparing adaptive search strategies of robots to the movement patterns of T cells lends insights into search efficiency in both systems.

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“…Multi-agent systems emphasize local interactions based on first principles, and these interactions give rise to the complex high-level emergent properties of interest. Such systems have been used to model biological phenomenon such as the human immune system [16], as well as solve real-world problems like communication between distributed radar transmitters [15] and efficient resource collection in swarms of foraging robots [11,12,14,13].…”

Section: Multi-agent Systemsmentioning

confidence: 99%

A Multi-agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing

Banerjee¹,

Hecker²

2016

Springer Proceedings in Complexity

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In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociates clusters to serve the changing resource demands of a global task queue. The algorithm is compared to a standard first-in first-out (FIFO) scheduling algorithm. Experiments done on a simulator show that the distributed resource allocation protocol (dRAP) algorithm outperforms the FIFO scheduling algorithm on time to empty queue, average waiting time and CPU utilization. Such a decentralized computing approach holds promise for massively distributed processing scenarios like SETI@home and Google MapReduce.

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Evolving Error Tolerance in Biologically-Inspired iAnt Robots

Cited by 8 publications

References 27 publications

From Microbiology to Microcontrollers: Robot Search Patterns Inspired by T Cell Movement

From Microbiology to Microcontrollers: Robot Search Patterns Inspired by T Cell Movement

Distinguishing Adaptive Search from Random Search in Robots and T cells

A Multi-agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing

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