Papers with strategic planning
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
| Approach: | They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives. |
| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
A Cost-Efficient Modular Sieve for Extracting Product Information from Company Websites (2024.emnlp-industry)
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Anna Hätty, Dragan Milchevski, Kersten Döring, Marko Putnikovic, Mohsen Mesgar, Filip Novović, Maximilian Braun, Karina Borimann, Igor Stranjanac
| Challenge: | Existing methods for extracting product information are resource-intensive and computationally prohibitive due to website structure differences and numerous non-product pages. |
| Approach: | They propose a modular method that leverages low-cost classification models to filter out company web pages. |
| Outcome: | The proposed method improves on a new dataset of 7000 product and non-product web pages and reduces computational time and costs. |
AVA: Attentive VLM Agent for Mastering StarCraft II (2026.findings-acl)
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| Challenge: | Existing StarCraft II benchmarks rely on abstract state representations that deviate from human perception . Existing systems rely only on abstract representations, creating an artificial gap between how humans process battlefield information and limiting ecological validity of learned behaviors. |
| Approach: | They introduce AVACraft, the first multimodal benchmark environment for complex decision-making in StarCraft II. |
| Outcome: | The AVACraft benchmark supports both traditional and modern multi-agent reinforcement learning paradigms. |
An Analysis of Dialogue Act Sequence Similarity Across Multiple Domains (2022.lrec-1)
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| Challenge: | a recent study shows that many machine learning models perform poorly when exposed to domain shifts due to contextual differences. |
| Approach: | They analyze dialogue act sequences from related domains to predict performance degradation . they find that when dialogue acts sequences are dissimilar they lie further away in embedding space . |
| Outcome: | The proposed model can be trained even when the datasets are corrupted with noise. |
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)
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| Challenge: | Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions. |
| Approach: | They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments. |
| Outcome: | The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments. |
Reinforced Target-driven Conversational Promotion (2023.emnlp-main)
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| Challenge: | Existing conversational recommendation methods focus on acquiring user preferences while ignoring strategic planning for nudging users towards accepting a designated item. |
| Approach: | They propose a Reinforced Target-driven Conversational Promotion framework that integrates short-term and long-term planning via a balanced gating mechanism. |
| Outcome: | The proposed model outperforms state-of-the-art models on automatic metrics and human evaluation. |
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play (2026.acl-long)
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Xiachong Feng, Deyi Yin, Xiaocheng Feng, Yi Jiang, Libo Qin, Yangfan Ye, Lei Huang, Weitao Ma, Qiming Li, Yuxuan Gu, Bing Qin, Lingpeng Kong
| Challenge: | Existing self-play approaches to developing general reasoning in language models rely on terminal game outcomes. |
| Approach: | They propose a game-based reasoning transfer model that addresses two barriers to reasoning transfer. |
| Outcome: | The proposed model improves mathematical reasoning, general reasoning, and code generation benchmarks. |
A Dual-Mind Framework for Strategic and Expressive Negotiation Agent (2025.acl-long)
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| Challenge: | Existing approaches to negotiation dialogue focus on only one aspect, ignoring the synergistic effect of their combined synergies. |
| Approach: | They propose a dual-mind negotiation agent framework that integrates an intuitive and a deliberative module for slow, expression optimization. |
| Outcome: | The proposed framework achieves state-of-the-art on negotiation datasets showing that it improves negotiation ability. |