Resolving Resource Incompatibilities in Intelligent Agents

• URL: http://arxiv.org/abs/2009.05898v1
• Date: Sun, 13 Sep 2020 02:09:04 GMT
• Title: Resolving Resource Incompatibilities in Intelligent Agents
• Authors: Mariela Morveli-Espinoza, Ayslan Possebom, and Cesar Augusto Tacla
• Abstract summary: In this paper, we focus on the incompatibilities that emerge due to resources limitations. We give an algorithm for identifying resource incompatibilities from a set of pursued goals and, on the other hand, we propose two ways for selecting those goals that will continue to be pursued.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: An intelligent agent may in general pursue multiple procedural goals simultaneously, which may lead to arise some conflicts (incompatibilities) among them. In this paper, we focus on the incompatibilities that emerge due to resources limitations. Thus, the contribution of this article is twofold. On one hand, we give an algorithm for identifying resource incompatibilities from a set of pursued goals and, on the other hand, we propose two ways for selecting those goals that will continue to be pursued: (i) the first is based on abstract argumentation theory, and (ii) the second based on two algorithms developed by us. We illustrate our proposal using examples throughout the article.

Related papers

• Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services [55.0337199834612]
  Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics.
  arXiv  Detail & Related papers  (2024-11-03T07:01:13Z)
• ROMA-iQSS: An Objective Alignment Approach via State-Based Value Learning and ROund-Robin Multi-Agent Scheduling [44.276285521929424]
  We introduce a decentralized state-based value learning algorithm that enables agents to independently discover optimal states. Our theoretical analysis shows that our approach leads decentralized agents to an optimal collective policy. Empirical experiments further demonstrate that our method outperforms existing decentralized state-based and action-based value learning strategies.
  arXiv  Detail & Related papers  (2024-04-05T09:39:47Z)
• SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving [64.38649623473626]
  Large Language Models (LLMs) have driven substantial progress in artificial intelligence. We propose a novel framework called textbfSEquential subtextbfGoal textbfOptimization (SEGO) to enhance LLMs' ability to solve mathematical problems.
  arXiv  Detail & Related papers  (2023-10-19T17:56:40Z)
• Setting the Right Expectations: Algorithmic Recourse Over Time [16.930905275894183]
  We propose an agent-based simulation framework for studying the effects of a continuously changing environment on algorithmic recourse. Our findings highlight that only a small set of specific parameterizations result in algorithmic recourse that is reliable for agents over time.
  arXiv  Detail & Related papers  (2023-09-13T14:04:15Z)
• On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring [105.13668993076801]
  A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees. We study this question in a general framework for interactive decision making with multiple agents. We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
  arXiv  Detail & Related papers  (2023-05-01T06:46:22Z)
• Artificial Intelligence and Dual Contract [2.1756081703276]
  We develop a model where two principals, each equipped with independent Q-learning algorithms, interact with a single agent. Our findings reveal that the strategic behavior of AI principals hinges crucially on the alignment of their profits.
  arXiv  Detail & Related papers  (2023-03-22T07:31:44Z)
• Lexicographic Multi-Objective Reinforcement Learning [65.90380946224869]
  We present a family of both action-value and policy gradient algorithms that can be used to solve such problems. We show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-12-28T10:22:36Z)
• Towards End-to-End Open Conversational Machine Reading [57.18251784418258]
  In open-retrieval conversational machine reading (OR-CMR) task, machines are required to do multi-turn question answering given dialogue history and a textual knowledge base. We model OR-CMR as a unified text-to-text task in a fully end-to-end style. Experiments on the ShARC and OR-ShARC dataset show the effectiveness of our proposed end-to-end framework.
  arXiv  Detail & Related papers  (2022-10-13T15:50:44Z)
• Neural-guided, Bidirectional Program Search for Abstraction and Reasoning [3.2348834229786885]
  This paper lays the foundations for two approaches to abstraction and reasoning not based in brute-force search. We first apply an existing program synthesis system called DreamCoder to create symbolic abstractions out of tasks solved so far. Second, we design a reasoning algorithm motivated by the way humans approach ARC.
  arXiv  Detail & Related papers  (2021-10-22T00:41:47Z)
• End-to-End Learning and Intervention in Games [60.41921763076017]
  We provide a unified framework for learning and intervention in games. We propose two approaches, respectively based on explicit and implicit differentiation. The analytical results are validated using several real-world problems.
  arXiv  Detail & Related papers  (2020-10-26T18:39:32Z)
• An Argumentation-based Approach for Identifying and Dealing with Incompatibilities among Procedural Goals [1.1744028458220426]
  An intelligent agent may generate multiple pursuable goals, which may be incompatible among them. In this paper, we focus on the definition, identification and resolution of these incompatibility.
  arXiv  Detail & Related papers  (2020-09-11T01:01:34Z)

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.