Automated negotiation, a form of interaction among autonomous agents, plays a central role in multi-agent systems, yet the application of large language model (LLM) in this domain remains underexplored. An LLM can serve as a meta-strategist, adaptively selecting explicit strategies for execution by external strategic tools based on its capabilities. We propose a negotiation AI agent equipped with explicit strategic tools, including time-dependent and tit-for-tat negotiation strategies. Our results show that strategic tool enhanced negotiators achieve approximately 16% higher average utility compared with baseline, latest LLM negotiators.

The Pickup-and-Delivery Problem with Time Windows (PDPTW) is a time-constrained variant of the vehicle-routing problem (VRP). Complex time constraints make it difficult to solve using existing NCO methods. In this paper, we present the Feasibility-Aware Masked Transformer (FAM-Trans) specialized for PDPTW. FAM-Trans integrates a lightweight side encoder with a context-aware embedding scheme that effectively captures temporal dependencies. A dynamic key-value module continuously updates node embeddings as the route progresses. During inference, a feasibility-guided post-inference filtering strategy suppresses constraint violations without post-hoc repair. Experiments on standard PDPTW benchmarks show that FAM-Trans outperforms NCO baselines by 20~35% in solution quality and constraint satisfaction.

We propose an automated negotiation for a reinforcement learning agent to adapt the agent to unexpected situations such as demand changes in supply chain management (SCM). Existing studies that consider reinforcement learning and SCM assume a centralized environment where the coordination of chain components is hierarchical rather than through negotiations between agents. This study focused on a negotiation agent that considered the value function of reinforcement learning for SCM as its utility function in automated negotiation. We demonstrated that the proposed approach could avoid inventory shortages under increased demand requests from the terminal customer.

We proposed a novel dual-process optimization approach for parts collection order and route planning in parts warehouses. Conventional multi-agent parts collection typically uses the vehicle routing problem (VRP), which focuses on minimizing the number of agents and costs. However, the model does not fully leverage the vehicle’s potential. Moreover, multi-agent path finding (MAPF) focuses on route planning and avoiding path conflicts, ignoring the order of part collection. The proposed approach integrates algorithms from the traveling salesman problem (TSP) and path planning, and modifies them to suit the dynamic and complex environment of parts warehouses. This integration streamlines the collection process and considerably reduces the operational time. Thus, the study can improve automation and efficiency in parts warehouse management and improve optimization techniques. The proposed method achieved more than tenfold acceleration compared with the ideal centralized optimization, without cost increments. As the number of agents and part collections increases, centralized optimization requires a metaheuristic approach, which results in solution degradation. However, the proposed approach maintains over tenfold acceleration and produces solutions with shorter operational times. Furthermore, we conducted an ablation study comparing six methods, from entirely independent to centralized optimization, demonstrating that the proposed approach effectively balances computational time and solution accuracy.

This study proposed a novel reward-based negotiating agent strategy using an issue-based represented deep policy network. We compared the negotiation strategies with reinforcement learning (RL) by the tournaments toward heuristics-based champion agents in multi-issue negotiation. A bilateral multi-issue negotiation in which the two agents exchange offers in turn was considered. Existing RL architectures for a negotiation strategy incorporate rich utility function that provides concrete information even though the rewards of RL are considered as generalized signals in practice. Additionally, in existing reinforcement learning architectures for negotiation strategies, both the issue-based representations of the negotiation problems and the policy network to improve the scalability of negotiation domains are yet to be considered. This study proposed a novel reward-based negotiation strategy through deep RL by considering an issue-based represented deep policy network for multi-issue negotiation. Comparative studies analyzed the significant properties of negotiation strategies with RL. The results revealed that the policy-based learning agents with issue-based representations achieved comparable or higher utility than the state-of-the-art baselines with RL and heuristics, especially in the large-sized domains. Additionally, negotiation strategies with RL based on the policy network can achieve agreements by effectively using each step.

Previous research on the comprehensive negotiation strategy using deep reinforcement learning (RL) has scalability issues of not performing effectively in the large-sized domains. We improve negotiation strategy via deep RL by considering an issue-based represented deep policy network to deal with multi-issue negotiation. The architecture of the proposed learning agent considers the characteristics of multi-issue negotiation domains and policy-based learning. We demonstrate that proposed method achieve equivalent or higher utility than existing negotiation agents in the large-sized domains.

Existing research in the field of automated negotiation considers a negotiation architecture in which some of the negotiation components are designed separately by reinforcement learning (RL), but comprehensive negotiation strategy design has not been achieved. In this study, we formulated an RL model based on a Markov decision process (MDP) for bilateral multi-issue negotiations. We propose a versatile negotiating agent that can effectively learn various negotiation strategies and domains through comprehensive strategies using deep RL. We show that the proposed method can achieve the same or better utility than existing negotiation agents.

This paper addresses the problem of path negotiation among self-interested multirobot operators in shared space. In conventional multirobot path planning problems, most of the research thus far has focused on the coordination and planning of collision-free paths for multiple robots with some common objectives. On the contrary, the recent progress of technologies in autonomous vehicles, including automated guidance vehicles, unmanned aerial vehicles, and manned autonomous cars, has increased demand for solving coordination and conflict avoidance in these autonomous and self-interested agents that pursue their own objectives. In this research, we tackle this problem from the operator perspective. We assume a problem setting where collisions between robots are avoided based on path reservation and negotiation. Under that circumstance, we propose a task-oriented utility function and a path negotiation algorithm for robot operators to maximize their task utility during path negotiation. The simulation and experiment results demonstrate the effectiveness of our task-based negotiation method over a simple path-based negotiation approach.