Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development

• URL: http://arxiv.org/abs/2408.15725v1
• Date: Wed, 28 Aug 2024 11:43:14 GMT
• Title: Different Facets for Different Experts: A Framework for Streamlining The Integration of Qualitative Insights into ABM Development
• Authors: Vivek Nallur, Pedram Aghaei, Graham Finlay,
• Abstract summary: Key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour.
• Score: 0.37240490024629924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A key problem in agent-based simulation is that integrating qualitative insights from multiple discipline experts is extremely hard. In most simulations, agent capabilities and corresponding behaviour needs to be programmed into the agent. We report on the architecture of a tool that disconnects the programmed functions of the agent, from the acquisition of capability and displayed behaviour. This allows multiple different domain experts to represent qualitative insights, without the need for code to be changed. It also allows a continuous integration (or even change) of qualitative behaviour processes, as more insights are gained. The consequent behaviour observed in the model is both, more faithful to the expert's insight as well as able to be contrasted against other models representing other insights.

Related papers

• SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging [60.83635006372403]
  textttSE-Merging is a self-enhanced model merging framework.<n>We show that textttSE-Merging achieves dynamic model merging without additional training.
  arXiv  Detail & Related papers  (2025-06-22T18:38:41Z)
• Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks [94.19506319646376]
  We introduce Agent-X, a benchmark for evaluating vision-centric agents in real-world, multimodal settings.<n>Agent-X features 828 agentic tasks with authentic visual contexts, including images, multi-image comparisons, videos, and instructional text.<n>Our results reveal that even the best-performing models, including GPT, Gemini, and Qwen families, struggle to solve multi-step vision tasks.
  arXiv  Detail & Related papers  (2025-05-30T17:59:53Z)
• Agentic AI Process Observability: Discovering Behavioral Variability [2.273531916003657]
  AI agents that leverage Large Language Models (LLMs) are increasingly becoming core building blocks of modern software systems.<n> frameworks enable the definition of agent setups using natural language prompting.<n>Within such setups, agent behavior is non-deterministic for any given input.
  arXiv  Detail & Related papers  (2025-05-26T15:26:07Z)
• Envisioning Beyond the Pixels: Benchmarking Reasoning-Informed Visual Editing [90.65399476233495]
  We introduce RISEBench, the first benchmark for evaluating Reasoning-Informed viSual Editing (RISE) RISEBench focuses on four key reasoning types: Temporal, Causal, Spatial, and Logical Reasoning. We propose an evaluation framework that assesses Instruction Reasoning, Appearance Consistency, and Visual Plausibility with both human judges and an LMM-as-a-judge approach.
  arXiv  Detail & Related papers  (2025-04-03T17:59:56Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions. Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes. We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning [0.41783829807634765]
  Multi-Agent Deep Reinforcement Learning (MADRL) was proven efficient in solving complex problems in robotics or games. This paper advocates for direct interpretability, generating post hoc explanations directly from trained models. We explore modern methods, including relevance backpropagation, knowledge edition, model steering, activation patching, sparse autoencoders and circuit discovery.
  arXiv  Detail & Related papers  (2025-02-02T09:15:27Z)
• Watson: A Cognitive Observability Framework for the Reasoning of Foundation Model-Powered Agents [7.392058124132526]
  Foundations models (FMs) play an increasingly prominent role in complex software systems, such as FM-powered agentic software (i.e., Agentware) Unlike traditional software, agents operate autonomously, using opaque data and implicit reasoning, making it difficult to observe and understand their behavior during runtime. We propose cognitive observability as a new type of required observability that has emerged for such innovative systems.
  arXiv  Detail & Related papers  (2024-11-05T19:13:22Z)
• Investigating the Role of Instruction Variety and Task Difficulty in Robotic Manipulation Tasks [50.75902473813379]
  This work introduces a comprehensive evaluation framework that systematically examines the role of instructions and inputs in the generalisation abilities of such models. The proposed framework uncovers the resilience of multimodal models to extreme instruction perturbations and their vulnerability to observational changes.
  arXiv  Detail & Related papers  (2024-07-04T14:36:49Z)
• Task-adaptive Q-Face [75.15668556061772]
  We propose a novel task-adaptive multi-task face analysis method named as Q-Face. Q-Face simultaneously performs multiple face analysis tasks with a unified model. Our method achieves state-of-the-art performance on face expression recognition, action unit detection, face attribute analysis, age estimation, and face pose estimation.
  arXiv  Detail & Related papers  (2024-05-15T03:13:11Z)
• DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning [84.22561239481901]
  We propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents. We evaluate DCIR in multiple environments including Multi-agent Particle, Google Research Football and StarCraft II Micromanagement.
  arXiv  Detail & Related papers  (2023-12-10T06:03:57Z)
• De-fine: Decomposing and Refining Visual Programs with Auto-Feedback [75.62712247421146]
  De-fine is a training-free framework that decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. Our experiments across various visual tasks show that De-fine creates more robust programs.
  arXiv  Detail & Related papers  (2023-11-21T06:24:09Z)
• Toward a Reasoning and Learning Architecture for Ad Hoc Teamwork [4.454557728745761]
  We present an architecture for ad hoc teamwork, which refers to collaboration in a team of agents without prior coordination. Our architecture combines the principles of knowledge-based and data-driven reasoning and learning. We use the benchmark simulated multiagent collaboration domain Fort Attack to demonstrate that our architecture supports adaptation to unforeseen changes.
  arXiv  Detail & Related papers  (2022-08-24T13:57:33Z)
• Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis [14.656957226255628]
  We introduce a model-agnostic method for discovery of behavior clusters in multiagent domains. Our framework makes no assumption about agents' underlying learning algorithms, does not require access to their latent states or models, and can be trained using entirely offline observational data.
  arXiv  Detail & Related papers  (2022-06-17T23:07:33Z)
• Diversifying Agent's Behaviors in Interactive Decision Models [11.125175635860169]
  Modelling other agents' behaviors plays an important role in decision models for interactions among multiple agents. In this article, we investigate into diversifying behaviors of other agents in the subject agent's decision model prior to their interactions.
  arXiv  Detail & Related papers  (2022-03-06T23:05:00Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)
• DirectDebug: Automated Testing and Debugging of Feature Models [55.41644538483948]
  Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact.
  arXiv  Detail & Related papers  (2021-02-11T11:22:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.