We address the problem of maritime traffic management in busy waterways to increase the safety of navigation by reducing congestion. We model maritime traffic as a large multiagent systems with individual vessels as agents, and VTS authority as the regulatory agent. We develop a maritime traffic simulator based on historical traffic data that incorporates realistic domain constraints such as uncertain and asynchronous movement of vessels. We also develop a traffic coordination approach that provides speed recommendation to vessels in different zones. We exploit the nature of collective interactions among agents to develop a scalable policy gradient approach that can scale up to real world problems. Empirical results on synthetic and real world problems show that our approach can significantly reduce congestion while keeping the traffic throughput high.
This paper proposes a multi agent based timetable scheduling algorithm for railway system which handles the in−between time delay of the newly introduced train. The delay management indeed optimizes the total journey time, hence increases the total utility of the whole railway system as well.Here we show that schedule generated by our proposed algorithm is the most optimized schedule. It is done by using the notion of DCOP(Distributed Constraint Optimization Problem), where we define some metric to analyze the system to achieve our goals. We use JADE(Java Agent DEvelopment Framework) platforms to simulate our work and test it using a small network. We also take a small case study to compare our proposed work with the existing one and the results are therefore presented.
Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multiagent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLMbased approximations of subgoals can achieve similar multiagent execution steps than those specified by human experts. Website and resources at glamor-usc.github.io/twostep LLM Subgoal Generator Prompt: < System prompt instruction > < Example of language subgoal description generation in an example domain >
Getting solution of a problem by group discussion is more reliable than getting solution from a single entity. To increase reliability of the solution, efficient group of experts should be formed. Here in this paper we have proposed a methodology for finding efficient team of experts for solution of a given problem collaboratively. The methodology is conceptualized with the notion of Multiagent system. We have considered several factors like Cardinality of team, ability, willingness, trust and reciprocality of an agent in order to construct the team. We have also proposed an optimization function which will be used to choose most eligible agents to form the team of experts.
We address the problem of robust decision making for stochastic network design. Our work is motivated by spatial conservation planning where the goal is to take management decisions within a fixed budget to maximize the expected spread of a population of species over a network of land parcels. Most previous work for this problem assumes that accurate estimates of different network parameters (edge activation probabilities, habitat suitability scores) are available, which is an unrealistic assumption. To address this shortcoming, we assume that network parameters are only partially known, specified via interval bounds. We then develop a decision making approach that computes the solution with minimax regret. We provide new theoretical results regarding the structure of the minmax regret solution which help develop a computationally efficient approach. Empirically, we show that previous approaches that work on point estimates of network parameters result in high regret on several standard benchmarks, while our approach provides significantly more robust solutions.
Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of interactions among agents, a feature commonly present in large scale multiagent applications; (b) a shaped reward model analytically derived from the learned reward model to address the key challenge of credit assignment; (c) a model-based multiagent RL approach that integrates shaped rewards into well known RL algorithms such as policy gradient, soft-actor critic. Compared to previous methods, our learned reward models are more accurate, and our approaches achieve better solution quality on synthetic and real world instances of air traffic control, and cooperative navigation with large agent population.
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