A Semi-Markov Decision Model for Recognizing the Destination of a Maneuvering Agent in Real Time Strategy Games

Recognizing destinations of a maneuvering agent is important in real time strategy games. Because finding path in an uncertain environment is essentially a sequential decision problem, we can model the maneuvering process by the Markov decision process (MDP). However, the MDP does not define an action duration. In this paper, we propose a novel semi-Markov decision model (SMDM). In the SMDM, the destination is regarded as a hidden state, which affects selection of an action; the action is affiliated with a duration variable, which indicates whether the action is completed. We also exploit a Rao-Blackwellised particle filter (RBPF) for inference under the dynamic Bayesian network structure of the SMDM. In experiments, we simulate agents’ maneuvering in a combat field and employ agents’ traces to evaluate the performance of our method. The results show that the SMDM outperforms another extension of the MDP in terms of precision, recall, and F-measure. Destinations are recognized efficiently by our method no matter whether they are changed or not. Additionally, the RBPF infer destinations with smaller variance and less time than the SPF. The average failure rates of the RBPF are lower when the number of particles is not enough

A Decentralized Partially Observable Markov Decision Model with Action Duration for Goal Recognition in Real Time Strategy Games

Multiagent goal recognition is a tough yet important problem in many real time strategy games or simulation systems. Traditional modeling methods either are in great demand of detailed agents’ domain knowledge and training dataset for policy estimation or lack clear definition of action duration. To solve the above problems, we propose a novel Dec-POMDM-T model, combining the classic Dec-POMDP, an observation model for recognizer, joint goal with its termination indicator, and time duration variables for actions with action termination variables. In this paper, a model-free algorithm named cooperative colearning based on Sarsa is used. Considering that Dec-POMDM-T usually encounters multiagent goal recognition problems with different sorts of noises, partially missing data, and unknown action durations, the paper exploits the SIS PF with resampling for inference under the dynamic Bayesian network structure of Dec-POMDM-T. In experiments, a modified predator-prey scenario is adopted to study multiagent joint goal recognition problem, which is the recognition of the joint target shared among cooperative predators. Experiment results show that (a) Dec-POMDM-T works effectively in multiagent goal recognition and adapts well to dynamic changing goals within agent group; (b) Dec-POMDM-T outperforms traditional Dec-MDP-based methods in terms of precision, recall, and F-measure

Identification of Lateral-Directional Aerodynamic Parameters for Aircraft Based on a Wind Tunnel Virtual Flight Test

In the early stages of aircraft design, a scaled model of the aircraft is installed in a wind tunnel for dynamic semi-free flight to approximate real flight, and the test data are then used to identify the aerodynamic parameters. However, the absence of the translational motion of the test model makes its flight dynamics different from those in free flight, and the effect of this difference on parameter identification needs to be investigated. To solve this problem, a 3-DOF wind tunnel virtual flight test device is built to fix the test model on a rotating mechanism, and the model is free to rotate in three axes through the deflection of the control surfaces. The flight dynamics equations for the wind tunnel virtual flight test are established and expressed as a decoupled form of the free flight force and the influence of the test support frame force on the model’s motions through linearization. The differences between wind tunnel virtual flight and free flight are analysed to develop a model for the identification of aerodynamic parameters. The selection of the lateral-directional excitation signal and the design method of its parameters are established based on the requirements for the identifiability of the aerodynamic derivatives, and a step-by-step method for the identification of aerodynamic force and moment derivatives is established. The aerodynamic parameter identification results of a blended wing body aircraft show that the identification method proposed in this paper can obtain results with high accuracy, and the response of the modified motion model is consistent with that of the free flight motion model

<i>Dendrobium officinale</i> Enzyme Changing the Structure and Behaviors of Chitosan/γ-poly(glutamic acid) Hydrogel for Potential Skin Care

Hydrogels have been widespreadly used in various fields. But weak toughness has limited their further applications. In this study, Dendrobium officinale enzyme (DOE) was explored to improve chitosan/γ-poly(glutamic acid) (CS/γ-PGA) hydrogel in the structure and properties. The results indicated that DOE with various sizes of ingredients can make multiple noncovalent crosslinks with the skeleton network of CS/γ-PGA, significantly changing the self-assembly of CS/γ-PGA/DOE hydrogel to form regular protuberance nanostructures, which exhibits stronger toughness and better behaviors for skin care. Particularly, 4% DOE enhanced the toughness of CS/γ-PGA/DOE hydrogel, increasing it by 116%. Meanwhile, water absorption, antioxygenation, antibacterial behavior and air permeability were increased by 39%, 97%, 27% and 52%

Carbon Monoxide Promotes the Catalytic Hydrogenation on Metal Cluster Catalysts

Size effect plays a crucial role in catalytic hydrogenation. The highly dispersed ultrasmall clusters with a limited number of metal atoms are one candidate of the next generation catalysts that bridge the single-atom metal catalysts and metal nanoparticles. However, for the unfavorable electronic property and their interaction with the substrates, they usually exhibit sluggish activity. Taking advantage of the small size, their catalytic property would be mediated by surface binding species. The combination of metal cluster coordination chemistry brings new opportunity. CO poisoning is notorious for Pt group metal catalysts as the strong adsorption of CO would block the active centers. In this work, we will demonstrate that CO could serve as a promoter for the catalytic hydrogenation when ultrasmall Pd clusters are employed. By means of DFT calculations, we show that Pdn n=2‐147 clusters display sluggish activity for hydrogenation due to the too strong binding of hydrogen atom and reaction intermediates thereon, whereas introducing CO would reduce the binding energies of vicinal sites, thus enhancing the hydrogenation reaction. Experimentally, supported Pd2CO catalysts are fabricated by depositing preestablished [Pd2(μ-CO)2Cl4]2- clusters on oxides and demonstrated as an outstanding catalyst for the hydrogenation of styrene. The promoting effect of CO is further verified experimentally by removing and reintroducing a proper amount of CO on the Pd cluster catalysts