36,153 research outputs found
Arguing Using Opponent Models
Peer reviewedPostprin
Discussing the Rules: Electronic Rulemaking and Democratic Deliberation
Of all recent efforts to transform the federal bureaucracy through the use of information technology, electronic rulemaking holds the most potential for enhancing the role of the public in policymaking. In its more expansive formulation, electronic rulemaking would allow citizens to learn about proposed regulations on the Web, comment on them electronically, read comments by others, and even discuss relevant issues with fellow citizens and agency staff. This paper outlines what we should expect from public involvement in electronic rulemaking, concluding that its promise lies in embedding democratic deliberation into administrative decisionmaking. The current move to put rulemaking dockets online, while important, is likely to fall short of electronic rulemaking’s potential. For important rules, electronic dockets should be supplemented with electronic dialogues, which support and encourage iterative discussions.rulemaking, public participation, electronic democracy, electronic dockets, deliberation, e-government
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System
In this paper, we propose a novel negotiation dialogue agent designed for the
online marketplace. Our agent is integrative in nature i.e, it possesses the
capability to negotiate on price as well as other factors, such as the addition
or removal of items from a deal bundle, thereby offering a more flexible and
comprehensive negotiation experience. We create a new dataset called
Integrative Negotiation Dataset (IND) to enable this functionality. For this
dataset creation, we introduce a new semi-automated data creation method, which
combines defining negotiation intents, actions, and intent-action simulation
between users and the agent to generate potential dialogue flows. Finally, the
prompting of GPT-J, a state-of-the-art language model, is done to generate
dialogues for a given intent, with a human-in-the-loop process for post-editing
and refining minor errors to ensure high data quality. We employ a set of novel
rewards, specifically tailored for the negotiation task to train our
Negotiation Agent, termed as the Integrative Negotiation Agent (INA). These
rewards incentivize the chatbot to learn effective negotiation strategies that
can adapt to various contextual requirements and price proposals. By leveraging
the IND, we train our model and conduct experiments to evaluate the
effectiveness of our reward-based dialogue system for negotiation. Our results
demonstrate that the proposed approach and reward system significantly enhance
the agent's negotiation capabilities. The INA successfully engages in
integrative negotiations, displaying the ability to dynamically adjust prices
and negotiate the inclusion or exclusion of items in a bundle dea
Negotiating with a logical-linguistic protocol in a dialogical framework
This book is the result of years of reflection. Some time ago, while working in
commodities, the author felt how difficult it was to decide the order in which to
use arguments during a negotiation process. What would happen if we translated the arguments into cards and played them according to the rules of the
Bridge game? The results were impressive. There was potential for improvement in the negotiation process. The investigation went deeper, exploring players, cards, deals and the information concealed in the players´ announcements,
in the cards and in the deals. This new angle brought the research to NeuroLinguistic Patterns and cryptic languages, such as Russian Cards.
In the following pages, the author shares her discovery of a new application for
Logical Dialogues: Negotiations, tackled from basic linguistic structures placed
under a dialogue form as a cognitive system which ‘understands’ natural language, with the aim to solve conflicts and even to serve peace
Towards Integration of Cognitive Models in Dialogue Management: Designing the Virtual Negotiation Coach Application
This paper presents an approach to flexible and adaptive dialogue management driven by cognitive modelling of human dialogue behaviour. Artificial intelligent agents, based on the ACT-R cognitive architecture, together with human actors are participating in a (meta)cognitive skills training within a negotiation scenario. The agent employs instance-based learning to decide about its own actions and to reflect on the behaviour of the opponent. We show that task-related actions can be handled by a cognitive agent who is a plausible dialogue partner. Separating task-related and dialogue control actions enables the application of sophisticated models along with a flexible architecture in which various alternative modelling methods can be combined. We evaluated the proposed approach with users assessing the relative contribution of various factors to the overall usability of a dialogue system. Subjective perception of effectiveness, efficiency and satisfaction were correlated with various objective performance metrics, e.g. number of (in)appropriate system responses, recovery strategies, and interaction pace. It was observed that the dialogue system usability is determined most by the quality of agreements reached in terms of estimated Pareto optimality, by the user's negotiation strategies selected, and by the quality of system recognition, interpretation and responses. We compared human-human and human-agent performance with respect to the number and quality of agreements reached, estimated cooperativeness level, and frequency of accepted negative outcomes. Evaluation experiments showed promising, consistently positive results throughout the range of the relevant scales
Be Selfish, But Wisely: Investigating the Impact of Agent Personality in Mixed-Motive Human-Agent Interactions
A natural way to design a negotiation dialogue system is via self-play RL:
train an agent that learns to maximize its performance by interacting with a
simulated user that has been designed to imitate human-human dialogue data.
Although this procedure has been adopted in prior work, we find that it results
in a fundamentally flawed system that fails to learn the value of compromise in
a negotiation, which can often lead to no agreements (i.e., the partner walking
away without a deal), ultimately hurting the model's overall performance. We
investigate this observation in the context of the DealOrNoDeal task, a
multi-issue negotiation over books, hats, and balls. Grounded in negotiation
theory from Economics, we modify the training procedure in two novel ways to
design agents with diverse personalities and analyze their performance with
human partners. We find that although both techniques show promise, a selfish
agent, which maximizes its own performance while also avoiding walkaways,
performs superior to other variants by implicitly learning to generate value
for both itself and the negotiation partner. We discuss the implications of our
findings for what it means to be a successful negotiation dialogue system and
how these systems should be designed in the future.Comment: Accepted at EMNLP 2023 (Main
- …