Interactive Decision-Making Tasks Overview

• Interactive decision-making tasks are defined as iterative, adaptive processes that integrate computational, cognitive, and socio-technical feedback to drive decision models in uncertain environments.
• They employ methodologies such as reinforcement learning, game-theoretic analysis, influence diagrams, and multi-agent architectures to optimize performance under resource constraints.
• Recent advances leverage large language models and tool-assisted agents to refine decision planning, enhance safety, and improve human-AI collaboration across various application domains.

Interactive decision-making tasks are computational, cognitive, or socio-technical processes in which decision models or agents select actions through iterative, feedback-driven, and often multi-agent or human-in-the-loop interactions. These tasks span domains such as R&D project evaluation, social networks, autonomous systems, human-AI collaboration, program synthesis dialog, and multi-agent medical reasoning. State-of-the-art research incorporates normative decision models, reinforcement learning, LLMs, stochastic game theory, and blackboard or multi-agent architectures to enable robust, adaptive, and context-sensitive decision-making under uncertainty, resource constraints, and diverse objectives.

1. Foundations and Models of Interactive Decision-Making

Fundamental to interactive decision-making is the explicit modeling of feedback, sequential interaction, and information acquisition. Traditional frameworks—such as influence diagrams used in normative decision analysis—have been operationalized via blackboard system designs, facilitating stepwise, opportunistic construction and revision of decision structures (Regan et al., 2013). These systems maintain a decision model (e.g., influence diagram) as a “blackboard” updated incrementally through specialized knowledge sources that encode both domain expertise and cross-cutting utilities (e.g., probabilistic assessment, units consistency).

Mathematically, the optimal decision policy is derived by maximizing expected utility:

$\max_x \sum_i p_i U(x_i)$

where $p_i$ denotes the probability of outcome $i$ and $U(x_i)$ is the utility function over outcomes.

Extensions to sequential decision-making incorporate resource constraints:

$$\begin{align*} \max & \sum_i p_i U(x_i) \ \text{subject to: } & \sum_i c_i \leq R \end{align*}$$

where $c_i$ represents the resource consumption and $R$ the available limit.

In reinforcement learning and bandit problems, the statistical complexity of adaptive, sample-efficient learning is captured by notions such as the Decision-Estimation Coefficient (DEC), which tightly characterizes the fundamental regret lower bounds and algorithmic limitations of interactive environments (Foster et al., 2021, Foster et al., 2023). This coefficient appears in meta-algorithms that reduce the interactive decision task to an online estimation problem, optimizing cumulative regret under information constraints.

2. Architectures and System Designs

Several architectural paradigms underpin interactive decision-making systems:

• Blackboard Architecture: All components (knowledge specialists, utilities, and control specialists) operate over a shared influence diagram, supporting opportunistic, incremental reasoning (e.g., R&D Analyst) (Regan et al., 2013).
• Game-Theoretic and Multi-Agent Frameworks: Systems modeling negotiation, competition, or cooperation among self-interested agents employ game-tree search, regret-matching, and matrix-game representations. For instance, in dense urban traffic, autonomous agents use game theory to plan interactive merges, leveraging intention abstraction and tree search approximations for computational tractability (Isele, 2019).
• Statistical Social Systems: Interactive decision-making in social networks exploits Bayesian social learning, random graph models, and SIS diffusion to trace collective sensing, influence, and coordination. Regret-matching in such networks is shown to converge to correlated equilibria and support adaptive coordination (Krishnamurthy et al., 2014).
• Human-AI and Dialog Systems: Recent systems leverage program synthesis to generate decision logic (e.g., eligibility checkers) that control information-gathering dialog, reducing hallucinations and optimizing the balance between accuracy and user burden (Toles et al., 26 Feb 2025).

A table summarizing several prominent architectures follows:

System	Architecture	Domain/Application
R&D Analyst	Blackboard, ID-based	R&D project evaluation (Regan et al., 2013)
Game-Tree AV	Stochastic Game Tree	Autonomous traffic negotiation (Isele, 2019)
DynamiCare	Dynamic Multi-Agent	Medical diagnostics (Shang et al., 3 Jul 2025)
ProADA	Program Synthesis	Eligibility dialog, decision support (Toles et al., 26 Feb 2025)
ChoiceMates	LLM Multi-Agent Dialog	Unfamiliar decision domains (Park et al., 2023)

3. Learning and Optimization in Interactive Contexts

Interactive decision-making commonly appears in sequential or iterative frameworks:

• Reinforcement Learning (RL) and Bandits: RL methods are central for learning in environments with unknown dynamics. Sample efficiency and adaptivity are formalized through the DEC, and instance-optimality can be achieved via hypothesis testing and active data collection (Foster et al., 2023, Dong et al., 2022). DRL-based frameworks for autonomous driving optimize not only reward but also safety, efficiency, and interpretability—criteria summarized in the DDTUI framework (Driving safety, Driving efficiency, Training efficiency, Unselfishness, Interpretability) (Tian et al., 3 Jan 2025).
• Imitation and Active Data Aggregation: Reward shaping plus imitation learning (e.g., with DAgger) can generate synthetic, human-like decision trajectories using minimal real demonstration data, validated via metrics such as trajectory similarity (METEOR score) in sequential game tasks (Brandt et al., 2023).
• Active Data Gathering: Off-policy and active relabeling procedures (hindsight relabeling) are essential for augmenting learning from sparse data and for efficient policy updates in compositional task settings, like VirtualHome or BabyAI (Li et al., 2022).

4. Human-AI Collaborative Decision-Making

Interactive decision-making frequently involves human-AI teams, where issues of trust, explanation, and performance are central:

• Interactive Explanations: Interactive, example-based explanations during onboarding phases help clinicians calibrate their reliance on AI, as shown in studies where the availability of nearest neighbor case visualizations led to higher rates of correct decisions and lower overreliance (Lee et al., 24 Sep 2024).
• Human-AI Complementarity: Despite increased interactivity or the provision of manipulable explanations, human-AI teams do not reliably outperform AI alone, particularly for in-distribution tasks; on out-of-distribution (OOD) data, the performance gap narrows, but careful interface design is needed to avoid reinforcing user biases (Liu et al., 2021).
• Preference Elicitation and Dialog: Hybrid LLM + constraint programming frameworks (e.g., MeetMate) iteratively construct formal constraints from natural language user input, optimizing subject-to evolving preferences (Lawless et al., 2023). Multi-agent dialog systems (e.g., ChoiceMates) promote diverse perspective exploration through orchestrated agent conversations, leading to improved user understanding in unfamiliar decision domains (Park et al., 2023).

5. Advances in LLM and Tool-Assisted Agents

Recent progress leverages LLMs for interactive and strategic decision-making but mitigates their limitations by tool-enhanced, modular designs:

• Structured Tool Use: Frameworks such as STRIDE decompose reasoning into sequential “Thought units” and offload computation (e.g., value iteration, dynamic programming updates) to specialized external tools with working memory, thereby improving LLM adherence to complex rules, long-term planning, and multi-agent game-theoretic reasoning (Li et al., 25 May 2024).
• Iterative Plan Optimization: AutoPlan augments LLM-based agents through explicit, iteratively refined natural-language plans, incorporating interaction histories, reflection (summarization, flaw identification, revision), and plan updating without requiring in-context demonstrations (Ouyang et al., 2023). This approach bridges misalignment between generic LLM knowledge and environment-specific task rules, achieving success rates competitive with or superior to demonstration-based baselines.

6. Multi-Agent and Open-Ended Interactive Decision-Making

Complex decision tasks often require flexible, multi-agent, open-ended architectures:

• Dynamic Multi-Agent Frameworks: Medical diagnostic systems like DynamiCare iteratively recruit and adjust teams of specialist agents, using collaborative protocols (voting, confidence scoring) and iteratively integrating patient–agent interactions to mirror real-world clinical reasoning (Shang et al., 3 Jul 2025). Specialist recruitment, dynamic team adjustment, and iterative log updates ($$\mathcal{L}^{(n+1)} = \mathcal{L}^{(n)} \cup \{ (q_n, a_n) \}$$) are critical for handling ambiguous or evolving patient scenarios.
• Scenario-Based Multi-Objective Optimization: In autonomous driving, DRL-based multi-agent coordination is benchmarked across scenario types (e.g., highways, roundabouts) and multiple DDTUI criteria, with hybrid approaches integrating rule-based safety modules into learning-based strategies to enhance both interpretability and overall system robustness (Tian et al., 3 Jan 2025).

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In summary, interactive decision-making tasks are characterized by sequential, adaptive, and often multi-agent engagement with uncertain environments. Methodological advances span from influence diagram-based blackboard systems and regret-minimizing game-theoretic learning to tool-assisted LLM agents, program synthesis for dialog, and dynamic multi-agent medical frameworks. Central research themes include the formalization of complexity and regret, the design of architectures for opportunistic and context-sensitive reasoning, optimization of learning and exploration, and the engineering of human-AI interfaces that support trust, transparency, and performance in collaborative settings. These directions collectively define the current landscape of research in interactive decision-making, with ongoing progress toward generalizable, explainable, and resource-efficient artificial intelligence systems.