169 research outputs found
Evolutionary robotics: model or design?
In this paper, I review recent work in evolutionary robotics (ER), and discuss the perspectives and future directions of the field. First, I propose to draw a crisp distinction between studies that exploit ER as a design methodology on the one hand, and studies that instead use ER as a modeling tool to better understand phenomena observed in biology. Such a distinction is not always that obvious in the literature, however. It is my conviction that ER would profit from an explicit commitment to one or the other approach. Indeed, I believe that the constraints imposed by the specific approach would guide the experimental design and the analysis of the results obtained, therefore reducing arbitrary choices and promoting the adoption of principled methods that are common practice in the target domain, be it within engineering or the life sciences. Additionally, this would improve dissemination and the impact of ER studies on other disciplines, leading to the establishment of ER as a valid tool either for design or modeling purposes
Swarm Cognition and Artificial Life
Abstract. Swarm Cognition is the juxtaposition of two relatively un-related concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the com-plexity of cognitive processes in the vertebrate brain. Recently, scientists from various disciplines suggest that, at a certain level of description, op-erational principles used to account for the behaviour of natural swarms may turn out to be extremely powerful tools to identify the neuroscien-tific basis of cognition. In this paper, we review the most recent studies in this direction, and propose an integration of Swarm Cognition with Artificial Life, identifying a roadmap for a scientific and technological breakthrough in Cognitive Sciences.
Collective decision making in distributed systems inspired by honeybees behaviour
We propose a design methodology to provide cognitive capabilities to large-scale artificial distributed systems. The behaviour of such systems is the result of non-linear interactions of the individuals with each other and with the environment, and the resulting system behaviour is in general difficult to predict. The proposed methodology is based on the concept of cognitive design patterns, that is, reusable solutions to tackle problems requiring cognitive abilities (e.g., decision-making, attention, categorisation). Cognitive design patterns aim to support the engineering of distributed systems through guidelines and theoretical models that link the individual control rules of the agents to the desired global behaviour. In this paper, we propose a cognitive design pattern for collective decision-making inspired by the nest-site selection behaviour of honeybee swarms. We describe and analyse the theoretical models, and distill a set of guidelines for the implementation of collective decisions in distributed multi-agent systems. We demonstrate the validity of the cognitive design pattern in a case study involving spatial factors: the collective selection of the shortest path between two target areas. We analyse the dynamics of the multi-agent system and we show a very good adherence with the predictions of the macroscopic model. Future refinements of the cognitive design pattern will allow its usage in different application domains
Evolutionary swarm robotics: a theoretical and methodological itinerary from individual neuro-controllers to collective behaviours
In the last decade, swarm robotics gathered much attention in the research community. By drawing inspiration from social insects and other self-organizing systems, it focuses on large robot groups featuring distributed control, adaptation, high robustness, and flexibility. Various reasons lay behind this interest in similar multi-robot systems. Above all, inspiration comes from the observation of social activities, which are based on concepts like division of labor, cooperation, and communication. If societies are organized in such a way in order to be more efficient, then robotic groups also could benefit from similar paradigms
A quantitative micro-macro link for collective decisions: the shortest path discovery/selection example
In this paper, we study how to obtain a quantitative correspondence between the dynamics of the microscopic implementation of a robot swarm and the dynamics of a macroscopic model of nest-site selection in honeybees. We do so by considering a collec- tive decision-making case study: the shortest path discovery/selection problem. In this case study, obtaining a quantitative correspondence between the microscopic and macroscopic dynamics-the so-called micro-macro link problem-is particularly challenging because the macroscopic model does not take into account the spatial factors inherent to the path discovery/selection problem. We frame this study in the context of a general engineering methodology that prescribes the inclusion of available theoretical knowledge about target macroscopic models into design patterns for the microscopic implementation. The attain- ment of the micro-macro link presented in this paper represents a necessary step towards the formalisation of a design pattern for collective decision making in distributed systems
Dealing with Expert Bias in Collective Decision-Making
Quite some real-world problems can be formulated as decision-making problems
wherein one must repeatedly make an appropriate choice from a set of
alternatives. Expert judgements, whether human or artificial, can help in
taking correct decisions, especially when exploration of alternative solutions
is costly. As expert opinions might deviate, the problem of finding the right
alternative can be approached as a collective decision making problem (CDM).
Current state-of-the-art approaches to solve CDM are limited by the quality of
the best expert in the group, and perform poorly if experts are not qualified
or if they are overly biased, thus potentially derailing the decision-making
process. In this paper, we propose a new algorithmic approach based on
contextual multi-armed bandit problems (CMAB) to identify and counteract such
biased expertises. We explore homogeneous, heterogeneous and polarised expert
groups and show that this approach is able to effectively exploit the
collective expertise, irrespective of whether the provided advice is directly
conducive to good performance, outperforming state-of-the-art methods,
especially when the quality of the provided expertise degrades. Our novel
CMAB-inspired approach achieves a higher final performance and does so while
converging more rapidly than previous adaptive algorithms, especially when
heterogeneous expertise is readily available
Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making
Experts advising decision-makers are likely to display expertise which varies
as a function of the problem instance. In practice, this may lead to
sub-optimal or discriminatory decisions against minority cases. In this work we
model such changes in depth and breadth of knowledge as a partitioning of the
problem space into regions of differing expertise. We provide here new
algorithms that explicitly consider and adapt to the relationship between
problem instances and experts' knowledge. We first propose and highlight the
drawbacks of a naive approach based on nearest neighbor queries. To address
these drawbacks we then introduce a novel algorithm - expertise trees - that
constructs decision trees enabling the learner to select appropriate models. We
provide theoretical insights and empirically validate the improved performance
of our novel approach on a range of problems for which existing methods proved
to be inadequate.Comment: Proceedings of the 40th International Conference on Machine Learning
(2023
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