Scalable Multiagent Coordination with Distributed Online Open Loop Planning

We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heuristic search in the space defined by a generative model of the domain dynamics, which is exploited by agents to simulate and evaluate the consequences of their potential choices. We also propose distributed online Thompson sampling (DOTS) as an effective instantiation of the DOOLP framework. DOTS models sequences of agent choices by concatenating a number of multiarmed bandits for each agent and uses Thompson sampling for dealing with action value uncertainty. The Bayesian approach underlying Thompson sampling allows to effectively model and estimate uncertainty about (a) own action values and (b) other agents' behavior. This approach yields a principled and statistically sound solution to the exploration-exploitation dilemma when exploring large search spaces with limited resources. We implemented DOTS in a smart factory case study with positive empirical results. We observed effective, robust and scalable planning and coordination capabilities even when only searching a fraction of the potential search space

Hierarchical Concurrent Engineering in a Multiagent Framework

Our experience indicates coordination in concurrent engineering (CE) requires support for two types of relationships among decision makers supervisor/subordinate and peer-to-peer Supervisor/subordinate relationships are created by the standard hierarchical decomposition process that is required to solve any large design problem Peer-to-peer relationships arise when teams of decision makers must interact, without direct guidance, to achieve individual and common goals In this paper, we describe a general decision-making methodology, which we call hierarchical CE The emphasis of hierarchical CE is to provide support for both supervisor/subordinate and peer-to-peer relationships In addition to the concept of hierarchical CE, we present a supporting agent-based framework in which the preferences and constraints of a design supervi sor are distributed to design subordinates, who are expected to exploit their local expertise within the context provided by this global information A distinct separation between feasibility and value facilitates optimal decision-making by design agents, since the bounds on feasibility do not include arbitrary statements about value This distinction may prove useful for other problem domains as wellPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68258/2/10.1177_1063293X9600400105.pd

Synchronicity:The Role of Midbrain Dopamine in Whole-Brain Coordination

Midbrain dopamine seems to play an outsized role in motivated behavior and learning. Widely associated with mediating reward-related behavior, decision making, and learning, dopamine continues to generate controversies in the field. While many studies and theories focus on what dopamine cells encode, the question of how the midbrain derives the information it encodes is poorly understood and comparatively less addressed. Recent anatomical studies suggest greater diversity and complexity of afferent inputs than previously appreciated, requiring rethinking of prior models. Here, we elaborate a hypothesis that construes midbrain dopamine as implementing a Bayesian selector in which individual dopamine cells sample afferent activity across distributed brain substrates, comprising evidence to be evaluated on the extent to which stimuli in the on-going sensorimotor stream organizes distributed, parallel processing, reflecting implicit value. To effectively generate a temporally resolved phasic signal, a population of dopamine cells must exhibit synchronous activity. We argue that synchronous activity across a population of dopamine cells signals consensus across distributed afferent substrates, invigorating responding to recognized opportunities and facilitating further learning. In framing our hypothesis, we shift from the question of how value is computed to the broader question of how the brain achieves coordination across distributed, parallel processing. We posit the midbrain is part of an “axis of agency” in which the prefrontal cortex (PFC), basal ganglia (BGS), and midbrain form an axis mediating control, coordination, and consensus, respectively

Experience-driven formation of parts-based representations in a model of layered visual memory

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

Coordination-Free Byzantine Replication with Minimal Communication Costs

State-of-the-art fault-tolerant and federated data management systems rely on fully-replicated designs in which all participants have equivalent roles. Consequently, these systems have only limited scalability and are ill-suited for high-performance data management. As an alternative, we propose a hierarchical design in which a Byzantine cluster manages data, while an arbitrary number of learners can reliable learn these updates and use the corresponding data. To realize our design, we propose the delayed-replication algorithm, an efficient solution to the Byzantine learner problem that is central to our design. The delayed-replication algorithm is coordination-free, scalable, and has minimal communication cost for all participants involved. In doing so, the delayed-broadcast algorithm opens the door to new high-performance fault-tolerant and federated data management systems. To illustrate this, we show that the delayed-replication algorithm is not only useful to support specialized learners, but can also be used to reduce the overall communication cost of permissioned blockchains and to improve their storage scalability