353,559 research outputs found
Projective simulation with generalization
The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
present a dynamic and autonomous machinery that enables projective simulation
agents to meaningfully generalize. Projective simulation, a novel, physical
approach to artificial intelligence, was recently shown to perform well in
standard reinforcement learning problems, with applications in advanced
robotics as well as quantum experiments. Both the basic projective simulation
model and the presented generalization machinery are based on very simple
principles. This allows us to provide a full analytical analysis of the agent's
performance and to illustrate the benefit the agent gains by generalizing.
Specifically, we show that already in basic (but extreme) environments,
learning without generalization may be impossible, and demonstrate how the
presented generalization machinery enables the projective simulation agent to
learn.Comment: 14 pages, 9 figure
A Study on the Parallelization of Terrain-Covering Ant Robots Simulations
Agent-based simulation is used as a tool for supporting (time-critical) decision making in differentiated contexts. Hence, techniques for speeding up the execution of agent-based models, such as Parallel Discrete Event Simulation (PDES), are of great relevance/benefit. On the other hand, parallelism entails that the final output provided by the simulator should closely match the one provided by a traditional sequential run. This is not obvious given that, for performance and efficiency reasons, parallel simulation engines do not allow the evaluation of global predicates on the simulation model evolution with arbitrary time-granularity along the simulation time-Axis. In this article we present a study on the effects of parallelization of agent-based simulations, focusing on complementary aspects such as performance and reliability of the provided simulation output. We target Terrain Covering Ant Robots (TCAR) simulations, which are useful in rescue scenarios to determine how many agents (i.e., robots) should be used to completely explore a certain terrain for possible victims within a given time. © 2014 Springer-Verlag Berlin Heidelberg
Projective simulation for artificial intelligence
We propose a model of a learning agent whose interaction with the environment
is governed by a simulation-based projection, which allows the agent to project
itself into future situations before it takes real action. Projective
simulation is based on a random walk through a network of clips, which are
elementary patches of episodic memory. The network of clips changes
dynamically, both due to new perceptual input and due to certain compositional
principles of the simulation process. During simulation, the clips are screened
for specific features which trigger factual action of the agent. The scheme is
different from other, computational, notions of simulation, and it provides a
new element in an embodied cognitive science approach to intelligent action and
learning. Our model provides a natural route for generalization to
quantum-mechanical operation and connects the fields of reinforcement learning
and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes
retaine
An Agent-Based Spatially Explicit Epidemiological Model in MASON
This paper outlines the design and implementation of an agent-based epidemiological simulation system. The system was implemented in the MASON toolkit, a set of Java-based agent-simulation libraries. This epidemiological simulation system is robust and extensible for multiple applications, including classroom demonstrations of many types of epidemics and detailed numerical experimentation on a particular disease. The application has been made available as an applet on the MASON web site, and as source code on the author\'s web site.Epidemiology, Social Networks, Agent-Based Simulation, MASON Toolkit
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