4 research outputs found
Swarm intelligence for autonomous cooperative agents in battles for real-time strategy games
This paper investigates the use the swarm intelligence of honey bees to create groups of co-operative AI for an RTS game in order to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns. An agent architecture and environment is proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated and their intelligence is tested using an adaptation of Anytime Universal Intelligence Test
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic Regression
Developing mathematical models of dynamic systems is central to many
disciplines of engineering and science. Models facilitate simulations, analysis
of the system's behavior, decision making and design of automatic control
algorithms. Even inherently model-free control techniques such as reinforcement
learning (RL) have been shown to benefit from the use of models, typically
learned online. Any model construction method must address the tradeoff between
the accuracy of the model and its complexity, which is difficult to strike. In
this paper, we propose to employ symbolic regression (SR) to construct
parsimonious process models described by analytic equations. We have equipped
our method with two different state-of-the-art SR algorithms which
automatically search for equations that fit the measured data: Single Node
Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In
addition to the standard problem formulation in the state-space domain, we show
how the method can also be applied to input-output models of the NARX
(nonlinear autoregressive with exogenous input) type. We present the approach
on three simulated examples with up to 14-dimensional state space: an inverted
pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep
neural networks and local linear regression shows that SR in most cases
outperforms these commonly used alternative methods. We demonstrate on a real
pendulum system that the analytic model found enables a RL controller to
successfully perform the swing-up task, based on a model constructed from only
100 data samples