اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدEnergy-Optimal Goal Assignment of Multi-Agent System with Goal Trajectories in Polynomials
60
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper, we propose an approach for solving an energy-optimal goal assignment problem to generate the desired formation in multi-agent systems. Each agent solves a decentralized optimization problem with only local information about its neighboring agents and the goals. The optimization problem consists of two sub-problems. The first problem seeks to minimize the energy for each agent to reach certain goals, while the second problem entreats an optimal combination of goal and agent pairs that minimizes the energy cost. By assuming the goal trajectories are given in a polynomial form, we prove the solution to the formulated problem exists globally. Finally, the effectiveness of the proposed approach is validated through the simulation.
قيم البحث
اقرأ أيضاً
This paper proposes a decentralized approach for solving the problem of moving a swarm of agents into a desired formation. We propose a decentralized assignment algorithm which prescribes goals to each agent using only local information. The assignme
nt results are then used to generate energy-optimal trajectories for each agent which have guaranteed collision avoidance through safety constraints. We present the conditions for optimality and discuss the robustness of the solution. The efficacy of the proposed approach is validated through a numerical case study to characterize the frameworks performance on a set of dynamic goals.
In this paper we introduce a qualitative decision and game theory based on belief (B) and desire (D) rules. We show that a group of agents acts as if it is maximizing achieved joint goals.
Most existing approaches for goal-oriented dialogue policy learning used reinforcement learning, which focuses on the target agent policy and simply treat the opposite agent policy as part of the environment. While in real-world scenarios, the behavi
or of an opposite agent often exhibits certain patterns or underlies hidden policies, which can be inferred and utilized by the target agent to facilitate its own decision making. This strategy is common in human mental simulation by first imaging a specific action and the probable results before really acting it. We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agents policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy. We evaluate our model on both cooperative and competitive dialogue tasks, showing superior performance over state-of-the-art baselines.
Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress ha
s been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called partial observability. An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.
Agent technology, a new paradigm in software engineering, has received attention from research and industry since 1990s. However, it is still not used widely to date because it requires expertise on both programming and agent technology; gaps among r
equirements, agent design, and agent deployment also pose more difficulties. Goal Net methodology attempts to solve these issues with a goal-oriented approach that resembles human behaviours, and an agent designer that supports agent development using this philosophy. However, there are limitations on existing Goal Net Designer, the design and modelling component of the agent designer. Those limitations, including limited access, difficult deployment, inflexibility in user operations, design workflows against typical Goal Net methodology workflows, and lack of data protection, have inhibited widespread adoption of Goal Net methodology. Motivated by this, this book focuses on improvements on Goal Net Designer. In this project, Goal Net Designer is completely re-implemented using new technology with optimised software architecture and design. It allows access from all major desktop operating systems, as well as in web environment via all modern browsers. Enhancements such as refined workflows, model validation tool, access control, team collaboration tool, and link to compiler make Goal Net Designer a fully functional and powerful Integrated Development Environment. User friendliness and usability are greatly enhanced by simplifying users actions to accomplish their tasks. User behaviour logging and quantitative feedback channel are also included to allow Goal Net Designer to continuously evolve with the power of big data analytics in future. To evaluate the new Goal Net Designer, a teachable agent has been developed with the help of Goal Net Designer and the development process is illustrated in a case study.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
