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From retina to behavior: prey-predator recognition by convolutional neural networks and their modulation by classical conditioning


Visual object-recognition plays a crucial role in animals that utilize visual information. In this study, we address the prey-predator recognition problem by optimizing artificial convolutional neural networks, based on neuroethological studies on toads. After the optimization of the overall network by supervised learning, the network showed a reasonable performance, even though various types of image noise existed. Also, we modulated the network after the optimization process based on the computational theory of classical conditioning and the reinforcement learning algorithm for the adaptation to environmental changes. This adaptation was implemented by separated modules that implement the "innate" term and "acquired" term of outputs. The modulated network exhibited behaviors that were similar to those of real toads. The neural basis of the amphibian visual information processing and the behavioral modulation mechanism have been substantially studied by biologists. Recent advances in parallel distributed processing technologies may enable us to develop fully autonomous, adaptive artificial agents with high-dimensional input spaces through end-to-end training methodology.

Intrinsically motivated particle swarm optimisation applied to task allocation for workplace hazard detection


This paper presents a framework for integrating intrinsic motivation with particle swarm optimisation. Intrinsically motivated particle swarm optimisation can be used for adaptive task allocation when the nature of the target task is not well understood in advance, or can change over time. We first present a general framework in which a computational model of motivation generates a dynamic fitness function to focus the attention of the particle swarm. We then discuss two approaches to modelling motivation in this framework: a computational model of curiosity using an unsupervised neural network and a model of novelty based on background subtraction. We introduce metrics for evaluating intrinsically motivated particle swarm optimisation and test our algorithm as an approach to task allocation in a workplace hazard mitigation scenario. We found that both proposed motivation techniques work well for generating a fitness function that can locate hazards, without requiring a precise definition of a hazard. We found that particle swarm optimisation can converge on optima in our generated fitness landscape in some, but not all, of our simulations.

A synergy of costly punishment and commitment in cooperation dilemmas


To ensure cooperation in the Prisoner’s Dilemma, individuals may require prior commitments from others, subject to compensations when agreements to cooperate are violated. Alternatively, individuals may prefer to behave reactively, without arranging prior commitments, by simply punishing those who misbehave. These two mechanisms have been shown to promote the emergence of cooperation, yet are complementary in the way they aim to promote cooperation. Although both mechanisms have their specific limitations, either one of them can overcome the problems of the other. On one hand, costly punishment requires an excessive effect-to-cost ratio to be successful, and this ratio can be significantly reduced by arranging a prior commitment with a more limited compensation. On the other hand, commitment-proposing strategies can be suppressed by free-riding strategies that commit only when someone else is paying the cost to arrange the deal, whom in turn can be dealt with more effectively by reactive punishers. Using methods from Evolutionary Game Theory, we present here an analytical model showing that there is a wide range of settings for which the combined strategy outperforms either strategy by itself, leading to significantly higher levels of cooperation. Interestingly, the improvement is most significant when the cost of arranging commitments is sufficiently high and the penalty reaches a certain threshold, thereby overcoming the weaknesses of both mechanisms.

A model of multi-agent consensus for vague and uncertain beliefs


Consensus formation is investigated for multi-agent systems in which agents’ beliefs are both vague and uncertain. Vagueness is represented by a third truth state meaning borderline. This is combined with a probabilistic model of uncertainty. A belief combination operator is then proposed, which exploits borderline truth values to enable agents with conflicting beliefs to reach a compromise. A number of simulation experiments are carried out, in which agents apply this operator in pairwise interactions, under the bounded confidence restriction that the two agents’ beliefs must be sufficiently consistent with each other before agreement can be reached. As well as studying the consensus operator in isolation, we also investigate scenarios in which agents are influenced either directly or indirectly by the state of the world. For the former, we conduct simulations that combine consensus formation with belief updating based on evidence. For the latter, we investigate the effect of assuming that the closer an agent’s beliefs are to the truth the more visible they are in the consensus building process. In all cases, applying the consensus operators results in the population converging to a single shared belief that is both crisp and certain. Furthermore, simulations that combine consensus formation with evidential updating converge more quickly to a shared opinion, which is closer to the actual state of the world than those in which beliefs are only changed as a result of directly receiving new evidence. Finally, if agent interactions are guided by belief quality measured as similarity to the true state of the world, then applying the consensus operator alone results in the population converging to a high-quality shared belief.