Olaf Witkowski

A Strategy for Origins of Life Research

Contents 1. Introduction 1.1. A workshop and this document 1.2. Framing origins of life science 1.2.1. What do we mean by the origins of life (OoL)? 1.2.2. Defining life 1.2.3. How should we characterize approaches to OoL science? 1.2.4. One path to life or many? 2. A Strategy for Origins of Life Research 2.1. Outcomes—key questions and investigations 2.1.1. Domain 1: Theory 2.1.2. Domain 2: Practice 2.1.3. Domain 3: Process 2.1.4. Domain 4: Future studies 2.2. EON Roadmap 2.3. Relationship to NASA Astrobiology Roadmap and Strategy documents and the European AstRoMap  Appendix I  Appendix II  Supplementary Materials  References

Emergence of Swarming Behavior: Foraging Agents Evolve Collective Motion Based on Signaling.

Swarming behavior is common in biology, from cell colonies to insect swarms and bird flocks. However, the conditions leading to the emergence of such behavior are still subject to research. Since Reynolds’ boids, many artificial models have reproduced swarming behavior, focusing on details ranging from obstacle avoidance to the introduction of fixed leaders. This paper presents a model of evolved artificial agents, able to develop swarming using only their ability to listen to each other’s signals. The model simulates a population of agents looking for a vital resource they cannot directly detect, in a 3D environment. Instead of a centralized algorithm, each agent is controlled by an artificial neural network, whose weights are encoded in a genotype and adapted by an original asynchronous genetic algorithm. The results demonstrate that agents progressively evolve the ability to use the information exchanged between each other via signaling to establish temporary leader-follower relations. These relations allow agents to form swarming patterns, emerging as a transient behavior that improves the agents’ ability to forage for the resource. Once they have acquired the ability to swarm, the individuals are able to outperform the non-swarmers at finding the resource. The population hence reaches a neutral evolutionary space which leads to a genetic drift of the genotypes. This reductionist approach to signal-based swarming not only contributes to shed light on the minimal conditions for the evolution of a swarming behavior, but also more generally it exemplifies the effect communication can have on optimal search patterns in collective groups of individuals.

Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space.

The Hunger Games: Embodied agents evolving foraging strategies on the frugal-greedy spectrum

In Evolutionary Biology and Game Theory, there is a long history of models aimed at predicting strategies adopted by agents during resource foraging. In Artificial Life, the agentbased modeling approach allowed to simulate the evolution of foraging behaviors in populations of artificial agents embodied in a simulated environment. In this paper, different sets of behaviors are evolved from a simple setting where agents seek for food patches distributed on a two-dimensional map. While agents are not explicitly playing a game of chicken, their strategies are found on a spectrum ranging from a frugal strategy (aka Dove) to a greedy strategy (aka Hawk). This phenomenon is due to the fact that moving is both a way for the agents to play or go to get away from an unfavorable area of the environment. It is also observed that by moving away, the agents preserve the ecology, preventing the resource from disappearing locally. Those strategies are shown to be stable if the environment is colonized by one given population. However, post-mortem tournaments among different groups of agents (separately evolved), systematically result in a specific group of agents dominating. The optimal strategy in the simulated tournaments is found to be one with fine-tuned timing for leaving. Further analysis shows how the strategy exploits resources without completely depleting them, producing Volterra-like population tendencies.

Pseudo-Static Cooperators: Moving Isn't Always about Going Somewhere

The evolution of cooperation has long been studied in Game Theory and Evolutionary Biology. In this study, we investigate the impact of movement control in a spatial version of the Prisoner’s Dilemma in a three dimensional space. A population of agents is evolved via an asynchronous genetic algorithm, to optimize their strategy. Our results show that cooperators rapidly join into static clusters, creating favorable niches for fast replications. Surprisingly, even though remaining inside those clusters, cooperators keep moving faster than defectors. We analyze the system dynamics to explain the stability of this behavior.

The dynamics of cooperation versus competition

The emergence and inter-play of cooperation versus competition in groups of individuals has been widely studied, for example using game-theoretic models of eusocial insects [11], [1] experimental evolution in bacterium [5], and agent-based models of societal institutions [8]. Game theory models have been demonstrated as indispensable analytical tools to complement our understanding of the emergence and social mechanics of natural phenomena such as cooperation and competition. For example, game theory models have supported the supposition that cooperation between individuals and competition between groups are critical factors in cultural evolution in human societies [2]. However, such game theory models are ultimately limited by their own abstractions and lack consideration for the role of complex phenomena such as evolutionary and environmental change in shaping emergent social phenomena.

The Transmission of Migratory Behaviors

In nature, animals rely upon migratory behaviors in order to adapt to seasonal variations in their environment. However, the transmission of migratory behaviors within populations (either during lifetimes or throughout successive generations) is not well understood (Bauer et al., 2011). In Artificial Life research, Agent Based Modeling (ABM) is a bottom-up approach to study evolutionary conditions under which adaptive group behavior emerges. ABM is characterized by synthetic methods (understanding via building), and is becoming increasingly popular in animal behavior research (Sumida et al., 1990). Combining an Artificial Neural Network (ANN) and Evolutionary Algorithm (EA) for adapting agent behavior (Yao, 1993) has received significant research attention (Phelps and Ryan, 2001), (Lee, 2003). ABM is an analogical system that aids ethologists in constructing novel hypotheses, and allow the investigation of emergent phenomena in experiments that could not be conducted in nature (Webb, 2009). Numerous studies in ethology have formalized mathematical models of migratory patterns in various species (Bauer et al., 2011). However, there have been few studies that examine ontological and phylogenetic conditions requisite for emergent migratory behavior. ABM is advantageous (compared to formal mathematical models of migratory behavior), since various evolutionary processes can be simulated, and variations in resultant migratory behaviors examined. For example, ABM has been used to predict the consequences of forced human migrations (Edwards, 2009), and migratory behavior between groups of Macaque monkeys (Hemelrijk, 2004). In this research, ABM is used to investigate a hypothesis posited in ethological literature: that migratory behavior is adopted as an adaptive foraging behavior, where such behavior is either genetically or culturally determined (Huse and Giske, 1998). This study aims to investigate the evolutionary and cultural conditions that give rise to migratory behaviors and thus adaptive foraging. In cultural behavioral transmission, ontogenetic transfer occurs between agents during their lifetime. Alternatively, migratory behavior is phylogenetically transmitted through successive generations (Bauer et al., 2011). A minimalist simulation model (distribution of four food patches and 200 agents on a grid) demonstrates the impact of ontogenetic versus phylogenetic transmission of migratory behavior and thus agent group adaptivity. Agents use an ANN controller (figure 1, left). ANN connection weights are adapted with an EA. Agent fitness is the food amount consumed during a lifetime (200 iterations). The EA selects for effective foraging behaviors, which depends upon agents periodically migrating to where food is plentiful. Stimuli for migratory behavior take the form of cyclic seasons in the environment and agents signaling their movement direction to neighbors. When it is winter (food is scarce) in one half of the environment, it is summer (food is plentiful) in the other half, where each seasonal cycle (50 iterations) the winter and summer zones are switched. Each iteration, agents receive the sensory inputs: signal from the closest agent, their current fitness and recurrent connections (activation value of the hidden layer in the previous iteration). Agent behavior is: move to an adjacent grid square, mimic or mate with a neighboring agent. The output with the highest activation is selected (figure 1, left). Each iteration, agents also emits a signal (output not depicted in figure 1), conveying the sender’s current direction of movement and thus indicating migratory behavior. Via choosing to mimic or mate, agents either imitate their neighbor’s migratory behaviors or pass genetically encoded migratory behaviors onto their offspring. If an agent mimics, it copies the ANN connection weights of its closest neighbor, thus mimicking its neighbors behavior, which includes the direction signal sent each iteration. If an agent mates, fitness proportionate selection (Eiben and Smith, 2003) is used to select a mate from the agent population. Genotypes (floating-point value strings) encoding the ANNs are recombined using 2-point crossover (Eiben and Smith, 2003). Two child ANNs are produced and replace the parents to keep the population size constant. If an agent moves, then it moves one grid cell north, south, east, or west (figure 1, left). Figure 1 (center and right) illustrates agent adaptation occurring over evolutionary time. Agents become effective gatherers via learning a migration behavior allowing them Late Breaking Papers