Shaofei Chen

National University of Defense Technology | NUDT · Department of Artificial Intelligence

Participatory sensing is a promising approach with which people contribute sensory information to form a body of knowledge. In practice, people may have different ways to engage in a participatory sensing campaign. For example, there are several possible routes from a participant’s home to her office, where a route can be seen as a set of space-tem...

Imperfect information game in multiplayer no-limit Texas Hold’em is a critical challenge in AI research. Recent advanced solving approaches, such as deep CounterFactual Value networks(CFVnet) combined with continual resolving, provide a way to conduct depth-limited search in imperfect-information games. However, CFVnet has limited deployment in Hea...

This study addressed a problem of rapid velocity consensus within a swarm of unmanned aerial vehicles. Our analytical framework was based on tools using matrix theory and algebraic graph theory. We established connections between algebraic connectivity and the speed of converging on a velocity. The relationship between algebraic connectivity and co...

In the development of artificial intelligence (AI), games have often served as benchmarks to promote remarkable breakthroughs in models and algorithms. No-limit Texas Hold’em (NLTH) is one of the most popular and challenging poker games. Despite numerous studies having been conducted on this subject, there are still some important problems that rem...

As the key technology of multi-agent game confrontation, opponent modeling is a typical cognitive modeling method of agent's behavior. Firstly, this article introduces several typical models of multi-agent game confrontation, non-stationary problems, and meta-game theory. Then, this paper summarizes opponent modeling methods, concludes the frontier...

Along with the great success of superhuman AI in succession, the development of Texas Hold'em poker agents is entering a new stage. The efforts to create indefectible AI transfers to develop new AIs which can exploit opponents and explain its own decisions better. Hand odds estimating used to state abstracting, situation evaluating, decision assist...

Battlefield situation comprehension (SC) plays an important role in the operation observation-orientation-decision-action (OODA) circle of UGV as the results of SC are the input of the UGV mission planning. A situation comprehension method for UGV based on domain knowledge is proposed to overcome the problems of heterogeneous situation data, variou...

Goal recognition (GR) is a method of inferring the goals of other agents, which enables humans or AI agents to proactively make response plans. Goal recognition design (GRD) has been proposed to deliberately redesign the underlying environment to accelerate goal recognition. Along with the GR and GRD problems, in this paper, we start by introducing...

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among...

This paper studies a search problem involving a robot that is searching for a certain item in an uncertain environment (e.g., searching minerals on Moon) that allows only limited interaction with humans. The uncertainty of the environment comes from the rewards of undiscovered items and the availability of costly human help. The goal of the robot i...

We investigate a decentralized patrolling problem for dynamic environments where information is distributed alongside threats. In this problem, agents obtain information at a location, but may suffer attacks from the threat at that location. In a decentralized fashion, each agent patrols in a designated area of the environment and interacts with a...

We investigate a multi-agent patrolling problem in large stochastic environments where information is distributed alongside threats. The information and threat at each location are respectively modelled as a multi-state Markov chain, whose states are not observed until the location is visited by an agent. While agents obtain information at a locati...

The stealth aircraft, studied in this article, plans a low observability trajectory to evade radars tracking, considering probability of detection and system constraints. An elaborate framework of planning low observability trajectory, which integrated the models of the stealth aircraft and radars, the theory of multi-phase optimal control and the...

The problem of planning flight trajectories is studied for multiple unmanned combat aerial vehicles (UCAVs) performing a cooperated air-to-ground target attack (CA/GTA) mission. Several constraints including individual and cooperative constraints are modeled, and an objective function is constructed. Then, the cooperative trajectory planning proble...

Purpose – The purpose of this paper is to plan the penetration trajectory for unmanned aerial vehicle (UAV) in the presence of radar‐guided surface to air missiles (SAMs). Design/methodology/approach – The penetration trajectory planning problem is modelled based on four aspects of radar tracking features. As penetration just utilizes the low obse...

In this paper, problem of planning tactical trajectory for stealth unmanned aerial vehicle (UAV) to win the radar game is studied. Three principles of how to win the radar game are presented, and their utilizations for stealth UAV to evade radar tracking are analysed. The problem is formulated by integrating the model of stealth UAV, the constraint...

Pseudospectral methods (PMs) for solving general optimal control problems (OCPs) attract an increasing amount of research and application in engineering. It is challenging to improve the convergence rate, the solution accuracy, and the applicability of PMs, especially for nonsmooth problems. Existing h p -adaptive PMs consider only one heuristic cr...

We propose a framework based on stochastic collocation to solve autonomous vehicle optimal trajectory planning problems with probabilistic uncertainty. We model uncertainty from the location and size of obstacles. We develop stochastic pseudospectral methods to solve the minimum expectation cost of differential equation, which meets path, control,...

Motion planning for unmanned combat aerial vehicle (UCAV) penetration in the presence of radar-guided surface to air missiles (SAMs) is studied. Firstly, the integrated model, involving nonlinear dynamic model and radar cross section (RCS) of UCAV, detection and tracking model of radar, is built. Four aspects of radar tracking features and their ut...