Deceptive path-planning is the task of finding a path so as to minimize the probability of an observer (or a defender) identifying the observed agent’s final goal before the goal has been reached. It is one of the important approaches to solving real-world challenges, such as public security, strategic transportation, and logistics. Existing methods either cannot make full use of the entire environments’ information, or lack enough flexibility for balancing the path’s deceptivity and available moving resource. In this work, building on recent developments in probabilistic goal recognition, we formalized a single real goal magnitude-based deceptive path-planning problem followed by a mixed-integer programming based deceptive path maximization and generation method. The model helps to establish a computable foundation for any further imposition of different deception concepts or strategies, and broadens its applicability in many scenarios. Experimental results showed the effectiveness of our methods in deceptive path-planning compared to the existing one.

The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human–AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human–AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.

Encoding multi-behavior information into a single graph collaborative filtering vector is an emerging challenge, as different behaviors generate distinct graphs, each with its own embedding vector. To address this problem, recent approaches typically designate the embedding of some behaviors as primary embeddings and use the embeddings of other behaviors to enhance the primary behavior recommendation. However, these models may excel in recommending primary behaviors at the expense of degrading the performance of auxiliary behaviors. As a result, modern recommender systems often need to maintain multiple sets of collaborative filtering embeddings to achieve satisfactory recommendation performance across all behaviors. To alleviate this issue, we introduce the Behavior Merging Graphs. Instead of modeling each behavior separately, BMG uses a joint graph to capture potential behavior merging sets between nodes and applies the partial order theory to model the intricate structures and relational order among behavior merging sets. Based on BMG, we introduce the Behavior Merging Graphs Convolutional Networks (BMGCN), which aggregates neighbor information by integrating convolutional weights that account for the rank transformation of Behavior Merging Order across various behavior merging sets. Furthermore, BMGCN employs behavior merging-based sampling to guide the traditional BPR sampling process, enhancing embedding training. Experiments on three widely used datasets demonstrate that BMGCN achieves superior multi-behavior recommendation performance compared to state-of-the-art baselines.

Deceptive path planning (DPP) aims to find a path that minimizes the probability of the observer identifying the real goal of the observed before it reaches. It is important for addressing issues such as public safety, strategic path planning, and logistics route privacy protection. Existing traditional methods often rely on “dissimulation”—hiding the truth—to obscure paths while ignoring the time constraints. Building upon the theory of probabilistic goal recognition based on cost difference, we proposed a DPP method, DPP_Q, based on count-based Q-learning for solving the DPP problems in discrete path-planning domains under specific time constraints. Furthermore, to extend this method to continuous domains, we proposed a new model of probabilistic goal recognition called the Approximate Goal Recognition Model (AGRM) and verified its feasibility in discrete path-planning domains. Finally, we also proposed a DPP method based on proximal policy optimization for continuous path-planning domains under specific time constraints called DPP_PPO. DPP methods like DPP_Q and DPP_PPO are types of research that have not yet been explored in the field of path planning. Experimental results show that, in discrete domains, compared to traditional methods, DPP_Q exhibits better effectiveness in enhancing the average deceptiveness of paths. (Improved on average by 12.53% compared to traditional methods). In continuous domains, DPP_PPO shows significant advantages over random walk methods. Both DPP_Q and DPP_PPO demonstrate good applicability in path-planning domains with uncomplicated obstacles.

Recognizing the destination of a maneuvering agent is important to create intelligent AI players in Real Time Strategy (RTS) games. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-the-shelf planners. However, the common problem in these frameworks is that they usually lack of the modeling of the action duration as in real-world scenarios the agent may take several steps to transit between grids. To solve this problem, a semi-Markov decision model (SMDM), which explicitly models the duration of an action, is proposed in this paper. Besides, most of the current works do not establish a behavioral model of the identified person, and there is almost no work modeling individual behavioral preference, which limits the accuracy of the recognition results. In this paper, the Inverse Reinforcement Learning (IRL) method is adopted in opponent behavior learning for the destination recognition problem. To adapt to the dynamic environment, the Maximum Entropy Inverse Reinforcement Learning (MaxEnt IRL) method is transformed by defining a Fitness index to measure the effect of weight and use the Nelder-Mead polyhedron search to find the optimal weight. In experiments, we build the game scenario in the Unreal Engine 4 environment and collect the moving trajectories from the human players in several different tasks for evaluating the performance of our methods. The results show that the recognizer using IRL can recognize the destination effectively even if the intention changes during the midway, and it performs better than other models in terms of several most frequently-used metrics.

The problem of goal recognition involves inferring the high-level task goals of an agent based on observations of its behavior in an environment. Current methods for achieving this task rely on offline comparison inference of observed behavior in discrete environments, which presents several challenges. First, accurately modeling the behavior of the observed agent requires significant computational resources. Second, continuous simulation environments cannot be accurately recognized using existing methods. Finally, real-time computing power is required to infer the likelihood of each potential goal. In this paper, we propose an advanced and efficient real-time online goal recognition algorithm based on deep reinforcement learning in continuous domains. By leveraging the offline modeling of the observed agent’s behavior with deep reinforcement learning, our algorithm achieves real-time goal recognition. We evaluate the algorithm’s online goal recognition accuracy and stability in continuous simulation environments under communication constraints.