132,227 research outputs found
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Search
Anytime multi-agent path finding (MAPF) is a promising approach to scalable
path optimization in large-scale multi-agent systems. State-of-the-art anytime
MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution
is iteratively optimized by destroying and repairing a fixed number of parts,
i.e., the neighborhood, of the solution, using randomized destroy heuristics
and prioritized planning. Despite their recent success in various MAPF
instances, current LNS-based approaches lack exploration and flexibility due to
greedy optimization with a fixed neighborhood size which can lead to low
quality solutions in general. So far, these limitations have been addressed
with extensive prior effort in tuning or offline machine learning beyond actual
planning. In this paper, we focus on online learning in LNS and propose
Bandit-based Adaptive LArge Neighborhood search Combined with Exploration
(BALANCE). BALANCE uses a bi-level multi-armed bandit scheme to adapt the
selection of destroy heuristics and neighborhood sizes on the fly during
search. We evaluate BALANCE on multiple maps from the MAPF benchmark set and
empirically demonstrate cost improvements of at least 50% compared to
state-of-the-art anytime MAPF in large-scale scenarios. We find that Thompson
Sampling performs particularly well compared to alternative multi-armed bandit
algorithms.Comment: Accepted to AAAI 202
Quantum inspired algorithms for learning and control of stochastic systems
Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.
A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover\u27s iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.
The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon stochastic optimal control problems. To numerically solve the stochastic Hamilton Jacobi Bellman equation, which characterizes the optimal expected cost function, large number of trajectory samples are required. The proposed methodology overcomes the above difficulty by using the path integral control formulation to adaptively sample trajectories of importance.
The third part of this dissertation presents two quantum inspired coordination models to dynamically assign targets to agents operating in a stochastic environment. The first approach uses a quantum decision theory model that explains irrational action choices in human decision making. The second approach uses a quantum game theory model that exploits the quantum mechanical phenomena \u27entanglement\u27 to increase individual pay-off in multi-player games. The efficiency and scalability of the proposed coordination models are demonstrated through simulations of a large scale multi-agent system --Abstract, page iii
Global adaptation in networks of selfish components: emergent associative memory at the system scale
In some circumstances complex adaptive systems composed of numerous self-interested agents can self-organise into structures that enhance global adaptation, efficiency or function. However, the general conditions for such an outcome are poorly understood and present a fundamental open question for domains as varied as ecology, sociology, economics, organismic biology and technological infrastructure design. In contrast, sufficient conditions for artificial neural networks to form structures that perform collective computational processes such as associative memory/recall, classification, generalisation and optimisation, are well-understood. Such global functions within a single agent or organism are not wholly surprising since the mechanisms (e.g. Hebbian learning) that create these neural organisations may be selected for this purpose, but agents in a multi-agent system have no obvious reason to adhere to such a structuring protocol or produce such global behaviours when acting from individual self-interest. However, Hebbian learning is actually a very simple and fully-distributed habituation or positive feedback principle. Here we show that when self-interested agents can modify how they are affected by other agents (e.g. when they can influence which other agents they interact with) then, in adapting these inter-agent relationships to maximise their own utility, they will necessarily alter them in a manner homologous with Hebbian learning. Multi-agent systems with adaptable relationships will thereby exhibit the same system-level behaviours as neural networks under Hebbian learning. For example, improved global efficiency in multi-agent systems can be explained by the inherent ability of associative memory to generalise by idealising stored patterns and/or creating new combinations of sub-patterns. Thus distributed multi-agent systems can spontaneously exhibit adaptive global behaviours in the same sense, and by the same mechanism, as the organisational principles familiar in connectionist models of organismic learning
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Naming Game on Adaptive Weighted Networks
We examine a naming game on an adaptive weighted network. A weight of
connection for a given pair of agents depends on their communication success
rate and determines the probability with which the agents communicate. In some
cases, depending on the parameters of the model, the preference toward
successfully communicating agents is basically negligible and the model behaves
similarly to the naming game on a complete graph. In particular, it quickly
reaches a single-language state, albeit some details of the dynamics are
different from the complete-graph version. In some other cases, the preference
toward successfully communicating agents becomes much more relevant and the
model gets trapped in a multi-language regime. In this case gradual coarsening
and extinction of languages lead to the emergence of a dominant language,
albeit with some other languages still being present. A comparison of
distribution of languages in our model and in the human population is
discussed.Comment: 22 pages, accepted in Artificial Lif
Biology of Applied Digital Ecosystems
A primary motivation for our research in Digital Ecosystems is the desire to
exploit the self-organising properties of biological ecosystems. Ecosystems are
thought to be robust, scalable architectures that can automatically solve
complex, dynamic problems. However, the biological processes that contribute to
these properties have not been made explicit in Digital Ecosystems research.
Here, we discuss how biological properties contribute to the self-organising
features of biological ecosystems, including population dynamics, evolution, a
complex dynamic environment, and spatial distributions for generating local
interactions. The potential for exploiting these properties in artificial
systems is then considered. We suggest that several key features of biological
ecosystems have not been fully explored in existing digital ecosystems, and
discuss how mimicking these features may assist in developing robust, scalable
self-organising architectures. An example architecture, the Digital Ecosystem,
is considered in detail. The Digital Ecosystem is then measured experimentally
through simulations, with measures originating from theoretical ecology, to
confirm its likeness to a biological ecosystem. Including the responsiveness to
requests for applications from the user base, as a measure of the 'ecological
succession' (development).Comment: 9 pages, 4 figure, conferenc
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