Agent-Exploitation Affordances: From Basic to Complex Representation Patterns

• URL: http://arxiv.org/abs/2607.07475v1
• Date: Wed, 08 Jul 2026 14:38:08 GMT
• Title: Agent-Exploitation Affordances: From Basic to Complex Representation Patterns
• Abstract summary: This work focuses on the concept of cooperative affordance within the realm of social affordances.<n>Cooperative affordances refer to situations where agents interact with each other to extend their action possibilities range.<n>We propose a tractable ontological representation of this concept with the aim of making it usable by an artificial agent.
• Score: 0.9176056742068814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In robotics, the capability of an artificial agent to represent the range of its action possibilities, i.e. affordances, is crucial to understand how it can act on its environment. While functional affordances, which refer to the use of tools and objects, have been broadly studied in knowledge representation, the implications of a social context and the presence of other agents have remained unexplored in this field. Consequently, in the field of social robotics, a multi-agent context enables the agents to engage in new actions that are potentially complementary to their individual capabilities, leading to the perspective of agentexploitation. This work focuses on the concept of cooperative affordance within the realm of social affordances. Cooperative affordances refer to situations where agents interact with each other to extend their action possibilities range. From this definition, this paper proposes a tractable ontological representation of this concept with the aim of making it usable by an artificial agent. Expanding on those elementary patterns, we illustrate the effectiveness of these representations by combining them to depict a diverse range of scenarios.

Related papers

• The Rise of AI Agent Communities: Large-Scale Analysis of Discourse and Interaction on Moltbook [62.2627874717318]
  Moltbook is a Reddit-like social platform where AI agents create posts and interact with other agents through comments and replies.<n>Using a public API snapshot collected about five days after launch, we address three research questions: what AI agents discuss, how they post, and how they interact.<n>We show that agents' writing is predominantly neutral, with positivity appearing in community engagement and assistance-oriented content.
  arXiv  Detail & Related papers  (2026-02-13T05:28:31Z)
• Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia [100.74015791021044]
  Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction.<n>Existing evaluation methods fail to measure how well these capabilities generalize to novel social situations.<n>We present empirical results from the NeurIPS 2024 Concordia Contest, where agents were evaluated on their ability to achieve mutual gains.
  arXiv  Detail & Related papers  (2025-12-03T00:11:05Z)
• Evolving Collective Cognition in Human-Agent Hybrid Societies: How Agents Form Stances and Boundaries [12.68373270583966]
  We investigate how group stance differentiation and social boundary formation emerge in human-agent hybrid societies.<n>We find that agents exhibit endogenous stances, independent of their preset identities.<n>Our findings suggest that preset identities do not rigidly determine the agents' social structures.
  arXiv  Detail & Related papers  (2025-08-24T13:50:18Z)
• A Desideratum for Conversational Agents: Capabilities, Challenges, and Future Directions [51.96890647837277]
  Large Language Models (LLMs) have propelled conversational AI from traditional dialogue systems into sophisticated agents capable of autonomous actions, contextual awareness, and multi-turn interactions with users.<n>This survey paper presents a desideratum for next-generation Conversational Agents - what has been achieved, what challenges persist, and what must be done for more scalable systems that approach human-level intelligence.
  arXiv  Detail & Related papers  (2025-04-07T21:01:25Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Agent AI: Surveying the Horizons of Multimodal Interaction [83.18367129924997]
  "Agent AI" is a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data. We envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
  arXiv  Detail & Related papers  (2024-01-07T19:11:18Z)
• Dimensions of Disagreement: Unpacking Divergence and Misalignment in Cognitive Science and Artificial Intelligence [11.236150405125754]
  The increasing prevalence of artificial agents creates an increasing need to manage disagreements between humans and artificial agents. Past studies in psychology have often cast disagreement as two agents forming diverging evaluations of the same object, but disagreement can also arise from differences in how agents represent that object. Understanding how divergence and misalignment interact to produce disagreement, and how resolution strategies depend on this interaction, is key to promoting effective collaboration between diverse types of agents.
  arXiv  Detail & Related papers  (2023-10-03T16:40:56Z)
• Towards socially-competent and culturally-adaptive artificial agents Expressive order, interactional disruptions and recovery strategies [0.0]
  The overarching aim of this work is to set a framework to make the artificial agent socially-competent beyond dyadic interaction-interaction. The paper highlights how this level of competence is achieved by focusing on just three dimensions: (i) social capability, (ii) relational role, and (iii) proximity.
  arXiv  Detail & Related papers  (2023-08-06T15:47:56Z)
• Towards a Unifying Model of Rationality in Multiagent Systems [11.321217099465196]
  Multiagent systems need to cooperate with other agents (including humans) nearly as effectively as these agents cooperate with one another. We propose a generic model of socially intelligent agents, which are individually rational learners that are also able to cooperate with one another. We show how we can construct socially intelligent agents for different forms of regret.
  arXiv  Detail & Related papers  (2023-05-29T13:18:43Z)
• 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)
• Building Affordance Relations for Robotic Agents - A Review [7.50722199393581]
  Affordances describe the possibilities for an agent to perform actions with an object. We review and find common ground amongst different strategies that use the concept of affordances within robotic tasks. We identify and discuss a range of interesting research directions involving affordances that have the potential to improve the capabilities of an AI agent.
  arXiv  Detail & Related papers  (2021-05-14T08:35:18Z)

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.