84,491 research outputs found
Decentralised Learning in Systems with Many, Many Strategic Agents
Although multi-agent reinforcement learning can tackle systems of
strategically interacting entities, it currently fails in scalability and lacks
rigorous convergence guarantees. Crucially, learning in multi-agent systems can
become intractable due to the explosion in the size of the state-action space
as the number of agents increases. In this paper, we propose a method for
computing closed-loop optimal policies in multi-agent systems that scales
independently of the number of agents. This allows us to show, for the first
time, successful convergence to optimal behaviour in systems with an unbounded
number of interacting adaptive learners. Studying the asymptotic regime of
N-player stochastic games, we devise a learning protocol that is guaranteed to
converge to equilibrium policies even when the number of agents is extremely
large. Our method is model-free and completely decentralised so that each agent
need only observe its local state information and its realised rewards. We
validate these theoretical results by showing convergence to Nash-equilibrium
policies in applications from economics and control theory with thousands of
strategically interacting agents
Studying complex adaptive systems using molecular classifier systems
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents occurring in a variety of natural and artificial systems (e.g. cells, societies, stock markets). These complex systems have the ability to adapt, evolve and learn from experience. To study CAS, Holland proposed to employ agent-based systems in which Learning Classifier Systems (LCS) are used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations
are reflexive in CAS, e.g. in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based artificial chemistry based on Hollandās Broadcast Language. In the MCS.b, no explicit fitness function is specified, moreover no distinction is made between messages and rules. In the
context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS : Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks
must replicate themselves prior to cell division. In this poster we present a series of experiments focusing on the self-replication ability of these CAS
Complex Adaptive Team Systems (CATS): Scaling of a Team Leadership Development Model
Complex adaptive systems (CAS) have been identified as being hard to comprehend, composed of multiple interacting components acting interdependently with overlapping functions aimed at adapting to external/environmental forces. The current theoretical model utilized the natural functions of teams, viewing teams as a complex adaptive system, to develop the structure of the theory of complex adaptive team systems (CATS). The CATS model was formulated around the components of complexity theory (interactions, nonlinearity, interdependency, heterogeneity, complex systems, emergence, self-organizing, and adaptability) to show its utility across multiple domains (the role of leadership, organizational learning, organizational change, collective cognitive structures, innovation, cross-business-unit collaborations). In theorizing the CATS model, a new level of analysis was implemented, the interactions between agents as a move toward emergence in complex systems. The CATS model ultimately provides a model for organizations/institutions to drive knowledge creation and innovation while operating in todayās complexity
Artificial societies and information theory: modelling of sub system formation based on Luhmann's autopoietic theory
This thesis develops a theoretical framework for the generation of artificial societies. In particular
it shows how sub-systems emerge when the agents are able to learn and have the ability
to communicate.
This novel theoretical framework integrates the autopoietic hypothesis of human societies, formulated
originally by the German sociologist Luhmann, with concepts of Shannon's information
theory applied to adaptive learning agents.
Simulations were executed using Multi-Agent-Based Modelling (ABM), a relatively new computational
modelling paradigm involving the modelling of phenomena as dynamical systems of
interacting agents. The thesis in particular, investigates the functions and properties necessary
to reproduce the paradigm of society by using the mentioned ABM approach.
Luhmann has proposed that in society subsystems are formed to reduce uncertainty. Subsystems
can then be composed by agents with a reduced behavioural complexity. For example in
society there are people who produce goods and other who distribute them.
Both the behaviour and communication is learned by the agent and not imposed. The simulated
task is to collect food, keep it and eat it until sated. Every agent communicates its energy state
to the neighbouring agents. This results in two subsystems whereas agents in the first collect
food and in the latter steal food from others. The ratio between the number of agents that
belongs to the first system and to the second system, depends on the number of food resources.
Simulations are in accordance with Luhmann, who suggested that adaptive agents self-organise
by reducing the amount of sensory information or, equivalently, reducing the complexity of the
perceived environment from the agent's perspective. Shannon's information theorem is used
to assess the performance of the simulated learning agents. A practical measure, based on the
concept of Shannon's information
ow, is developed and applied to adaptive controllers which
use Hebbian learning, input correlation learning (ICO/ISO) and temporal difference learning.
The behavioural complexity is measured with a novel information measure, called Predictive
Performance, which is able to measure at a subjective level how good an agent is performing
a task. This is then used to quantify the social division of tasks in a social group of honest,
cooperative food foraging, communicating agents
A molecular approach to complex adaptive systems
Complex Adaptive Systems (CAS) are dynamical networks of interacting agents which as a whole determine the behavior, adaptivity and cognitive ability of the system. CAS are
ubiquitous and occur in a variety of natural and artificial systems (e.g., cells, societies, stock markets). To study CAS, Holland proposed to employ an agent-based system in which Learning Classifier Systems (LCS) were used to determine the agents behavior and adaptivity. We argue that LCS are limited for the study of CAS: the rule-discovery mechanism is pre-specified and may limit the evolvability of CAS. Secondly, LCS distinguish a demarcation between messages and rules, however operations are reflexive in CAS, e.g., in a cell, an agent (a molecule) may both act as a message (substrate) and as a catalyst (rule). To address these issues, we proposed the Molecular Classifier Systems (MCS.b), a string-based Artificial Chemistry based on Hollandās broadcast language. In the MCS.b, no explicit fitness function or rule discovery mechanism is specified, moreover no distinction is made between messages and rules. In the context of the ESIGNET project, we employ the MCS.b to study a subclass of CAS: Cell Signaling Networks (CSNs) which are complex biochemical networks responsible for coordinating cellular activities. As CSNs occur in cells, these networks must replicate themselves prior to cell division. In this paper we present a series of experiments
focusing on the self-replication ability of these CAS. Results indicate counter intuitive outcomes as opposed to those inferred from the literature. This work highlights the current deficit of a theoretical framework for the study of Artificial Chemistries
No-Regret Learning and Equilibrium Computation in Quantum Games
As quantum processors advance, the emergence of large-scale decentralized
systems involving interacting quantum-enabled agents is on the horizon. Recent
research efforts have explored quantum versions of Nash and correlated
equilibria as solution concepts of strategic quantum interactions, but these
approaches did not directly connect to decentralized adaptive setups where
agents possess limited information. This paper delves into the dynamics of
quantum-enabled agents within decentralized systems that employ no-regret
algorithms to update their behaviors over time. Specifically, we investigate
two-player quantum zero-sum games and polymatrix quantum zero-sum games,
showing that no-regret algorithms converge to separable quantum Nash equilibria
in time-average. In the case of general multi-player quantum games, our work
leads to a novel solution concept, (separable) quantum coarse correlated
equilibria (QCCE), as the convergent outcome of the time-averaged behavior
no-regret algorithms, offering a natural solution concept for decentralized
quantum systems. Finally, we show that computing QCCEs can be formulated as a
semidefinite program and establish the existence of entangled (i.e.,
non-separable) QCCEs, which cannot be approached via the current paradigm of
no-regret learning
Analytics and complexity: learning and leading for the future
There is growing interest in the application of learning analytics to manage, inform and improve learning and teaching within higher education. In particular, learning analytics is seen as enabling data-driven decision making as universities are seeking to respond a range of significant challenges that are reshaping the higher education landscape. Experience over four years with a project exploring the use of learning analytics to improve learning and teaching at a particular university has, however, revealed a much more complex reality that potentially limits the value of some analytics-based strategies. This paper uses this experience with over 80,000 students across three learning management systems, combined with literature from complex adaptive systems and learning analytics to identify the source and nature of these limitations along with a suggested path forward
Dynamics of Interacting Neural Networks
The dynamics of interacting perceptrons is solved analytically. For a
directed flow of information the system runs into a state which has a higher
symmetry than the topology of the model. A symmetry breaking phase transition
is found with increasing learning rate. In addition it is shown that a system
of interacting perceptrons which is trained on the history of its minority
decisions develops a good strategy for the problem of adaptive competition
known as the Bar Problem or Minority Game.Comment: 9 pages, 3 figures; typos corrected, content reorganize
Stability and Diversity in Collective Adaptation
We derive a class of macroscopic differential equations that describe
collective adaptation, starting from a discrete-time stochastic microscopic
model. The behavior of each agent is a dynamic balance between adaptation that
locally achieves the best action and memory loss that leads to randomized
behavior. We show that, although individual agents interact with their
environment and other agents in a purely self-interested way, macroscopic
behavior can be interpreted as game dynamics. Application to several familiar,
explicit game interactions shows that the adaptation dynamics exhibits a
diversity of collective behaviors. The simplicity of the assumptions underlying
the macroscopic equations suggests that these behaviors should be expected
broadly in collective adaptation. We also analyze the adaptation dynamics from
an information-theoretic viewpoint and discuss self-organization induced by
information flux between agents, giving a novel view of collective adaptation.Comment: 22 pages, 23 figures; updated references, corrected typos, changed
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