12 research outputs found
Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents’ usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing
the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem
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Talking Helps: Evolving Communicating Agents for the Predator-Prey Pursuit Problem
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to evolve multi-agent languages for the predator agents in a version of the predator-prey pursuit problem. We show that the resulting behavior of the communicating multi-agent system is equivalent to that of a Mealy finite state machine whose states are determined by the agents' usage of the evolved language. Simulations show that the evolution of a communication language improves the performance of the predators. Increasing the language size (and thus increasing the number of possible states in the Mealy machine) improves the performance even further. Furthermore, the evolved communicating predators perform significantly better than all previous work on similar preys. We introduce a method for incrementally increasing the language size which results in an effective coarse-to-fine search that significantly reduces the evolution time required to find a solution. We present some observations on the effects of language size, experimental setup, and prey difficulty on the evolved Mealy machines. In particular, we observe that the start state is often revisited, and incrementally increasing the language size results in smaller Mealy machines. Finally, a simple rule is derived that provides a pessimistic estimate on the minimum language size that should be used for any multi-agent problem.
Learning Communication for Multi-agent Systems
We analyze a general model of multi-agent communication in which all agents communicate simultaneously to a message board. A genetic algorithm is used to learn multi-agent languages for the predator agents in a version of the predator-prey problem. The resulting evolved behavior of the communicating multi-agent system is equivalent to that of a Mealy machine whose states are determined by the evolved language. We also constructed non-learning predators whose capture behavior was designed to take advantage of prey behavior known a priori. Simulations show that introducing noise to the decision process of the hard-coded predators allow them to significantly ourperform all previously published work on similar preys. Furthermore, the evolved communicating predators were able to perform significantly better than the hard-coded predators, which indicates that the system was able to learn superior communicating strategies not readily available to the human designer
An Analysis of Noise in Recurrent Neural Networks: Convergence and Generalization
There has been much interest in applying noise to feedforward neural networks in order to observe their effect on network performance. We extend these results by introducing and analyzing various methods of injecting synaptic noise into dynamically-driven recurrent networks during training. We present theoretical results which show that applying a controlled amount of noise during training may improve convergence and generalization performance. In addition, we analyze the effects of various noise parameters (additive vs. multiplicative, cumulative vs. non-cumulative, per time step vs. per string) and predict that best overall performance can be achieved by injecting additive noise at each time step. Noise contributes a second-order gradient term to the error function which can be viewed as an anticipatory agent to aid convergence. This term appears to find promising regions of weight space in the beginning stages of training when the training error is large and should improve convergen..
Structure-Based Design Technology Contour and Its Application to the Design of Renin Inhibitors
It is well-known that the structure-based design approach
has had
a measurable impact on the drug discovery process in identifying novel
and efficacious therapeutic agents for a variety of disease targets.
The de novo design approach has inherent potential to generate novel
molecules that best fit into a protein binding site when compared
to all of the computational methods applied to structure-based design.
In its initial attempts, this approach did not achieve much success
due to technical hurdles. More recently, the algorithmic advancements
in the methodologies and clever strategies developed to design drug-like
molecules have improved the success rate. We describe a state-of-the-art
structure-based design technology called Contour and provide details
of the algorithmic enhancements we have implemented. Contour was designed
to create novel drug-like molecules by assembling synthetically viable
fragments in the protein binding site using a high-resolution crystal
structure of the protein. The technology consists of a sophisticated
growth algorithm and a novel scoring function based on a directional
model. The growth algorithm generates molecules by dynamically selecting
only those fragments from the fragment library that are complementary
to the binding site, and assembling them by sampling the conformational
space for each attached fragment. The scoring function embodying the
essential elements of the binding interactions aids in the rank ordering
of grown molecules and helps identify those that have high probability
of exhibiting activity against the protein target of interest. The
application of Contour to identify inhibitors against human renin
enzyme eventually leading to the clinical candidate VTP-27,999 will
be discussed here