15 research outputs found
Pruning of genetic programming trees using permutation tests
We present a novel approach based on statistical permutation tests for pruning redundant subtrees from genetic programming (GP) trees that allows us to explore the extent of effective redundancy . We observe that over a range of regression problems, median tree sizes are reduced by around 20% largely independent of test function, and that while some large subtrees are removed, the median pruned subtree comprises just three nodes; most take the form of an exact algebraic simplification. Our statistically-based pruning technique has allowed us to explore the hypothesis
that a given subtree can be replaced with a constant if this substitution results in no statistical change to the behavior of the parent tree – what we term approximate simplification. In the eventuality, we infer that more than 95% of the accepted pruning proposals are the result of algebraic simplifications, which provides some practical insight into the scope of removing redundancies in GP trees
Star-forming cores embedded in a massive cold clump: Fragmentation, collapse and energetic outflows
The fate of massive cold clumps, their internal structure and collapse need
to be characterised to understand the initial conditions for the formation of
high-mass stars, stellar systems, and the origin of associations and clusters.
We explore the onset of star formation in the 75 M_sun SMM1 clump in the region
ISOSS J18364-0221 using infrared and (sub-)millimetre observations including
interferometry. This contracting clump has fragmented into two compact cores
SMM1 North and South of 0.05 pc radius, having masses of 15 and 10 M_sun, and
luminosities of 20 and 180 L_sun. SMM1 South harbours a source traced at 24 and
70um, drives an energetic molecular outflow, and appears supersonically
turbulent at the core centre. SMM1 North has no infrared counterparts and shows
lower levels of turbulence, but also drives an outflow. Both outflows appear
collimated and parsec-scale near-infrared features probably trace the
outflow-powering jets. We derived mass outflow rates of at least 4E-5 M_sun/yr
and outflow timescales of less than 1E4 yr. Our HCN(1-0) modelling for SMM1
South yielded an infall velocity of 0.14 km/s and an estimated mass infall rate
of 3E-5 M_sun/yr. Both cores may harbour seeds of intermediate- or high-mass
stars. We compare the derived core properties with recent simulations of
massive core collapse. They are consistent with the very early stages dominated
by accretion luminosity.Comment: Accepted for publication in ApJ, 14 pages, 7 figure
Sampling Bias in Estimation of Distribution Algorithms for Genetic Programming Using Prototype Trees
Predicting Player Trajectories in Shot Situations in Soccer
Player behaviors can have a significant impact on the outcome of individual events, as well as the game itself. The increased availability of high quality resolution spatio-temporal data has enabled analysis of player behavior and game strategy. In this paper, we present the implementation and evaluation of an imitation learning method using recurrent neural networks, which allows us to learn individual player behaviors and perform rollouts of player movements on previously unseen play sequences. The method is evaluated using a 2019 dataset from the top-tier soccer league in Sweden (Allsvenskan). Our evaluation provides insights how to best apply the method on movement traces in soccer, the relative accuracy of the method, and how well policies of one player role capture the relative behaviors of a different player role, for example
Cooperative Learning Using Advice Exchange
One of the main questions concerning learning in a Multi-Agent System's environment is: "(How) can agents benefit from mutual interaction during the learning process?" This paper describes a technique that enables a heterogeneous group of Learning Agents (LAs) to improve its learning performance by exchanging advice. This technique uses supervised learning (backpropagation), where the desired response is not given by the environment but is based on advice given by peers with better performance score. The LAs are facing problems with similar structure, in environments where only reinforcement information is available. Each LA applies a different, well known, learning technique. The problem used for the evaluation of LAs performance is a simplified trafficcontrol simulation. In this paper the reader can find a summarized description of the traffic simulation and Learning Agents (focused on the advice-exchange mechanism), a discussion of the first results obtained and suggested techniques to overcome the problems that have been observed
Shaping Multi-Agent Systems with Gradient Reinforcement Learning
The original publication is available at www.springerlink.comInternational audienceAn original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task of automatically building a coordinated system is of crucial importance. To that end, we design simple reactive agents in a decentralized way as independent learners. But to cope with the difficulties inherent to RL used in that framework, we have developed an incremental learning algorithm where agents face a sequence of progressively more complex tasks. We illustrate this general framework by computer experiments where agents have to coordinate to reach a global goal