Penalization Framework For Autonomous Agents Using Answer Set Programming

• URL: http://arxiv.org/abs/2309.04487v1
• Date: Wed, 30 Aug 2023 09:09:27 GMT
• Title: Penalization Framework For Autonomous Agents Using Answer Set Programming
• Authors: Vineel S. K. Tummala
• Abstract summary: This paper presents a framework for enforcing penalties on intelligent agents that do not comply with authorization or obligation policies in a changing environment. A framework is proposed to represent and reason about penalties in plans, and an algorithm is proposed to penalize an agent's actions based on their level of compliance with respect to authorization and obligation policies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a framework for enforcing penalties on intelligent agents that do not comply with authorization or obligation policies in a changing environment. A framework is proposed to represent and reason about penalties in plans, and an algorithm is proposed to penalize an agent's actions based on their level of compliance with respect to authorization and obligation policies. Being aware of penalties an agent can choose a plan with a minimal total penalty, unless there is an emergency goal like saving a human's life. The paper concludes that this framework can reprimand insubordinate agents.

Related papers

• SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
  We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
  arXiv  Detail & Related papers  (2026-02-15T16:09:48Z)
• Are Your Agents Upward Deceivers? [73.1073084327614]
  Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users.<n>This raises the question of whether they may also engage in deception, similar to how individuals in human organizations lie to superiors to create a good image or avoid punishment.<n>We observe and define agentic upward deception, a phenomenon in which an agent facing environmental constraints conceals its failure and performs actions that were not requested without reporting.
  arXiv  Detail & Related papers  (2025-12-04T14:47:05Z)
• Autonomous Agents and Policy Compliance: A Framework for Reasoning About Penalties [1.2891210250935148]
  This paper presents a logic programming-based framework for policy-aware autonomous agents that can reason about potential penalties for non-compliance.<n>Our framework extends Gelfond and Lobo's Authorization and Obligation Policy Language (AOPL) to incorporate penalties.<n>Our method ensures well-formed policies, accounts for policy priorities, and enhances explainability by explicitly identifying rule violations.
  arXiv  Detail & Related papers  (2025-12-03T16:29:09Z)
• Effective Red-Teaming of Policy-Adherent Agents [7.080204863156575]
  Task-oriented LLM-based agents are increasingly used in domains with strict policies, such as refund eligibility or cancellation rules.<n>We propose a novel threat model that focuses on adversarial users aiming to exploit policy-adherent agents for personal benefit.<n>We present CRAFT, a multi-agent red-teaming system that leverages policy-aware persuasive strategies to undermine a policy-adherent agent in a customer-service scenario.
  arXiv  Detail & Related papers  (2025-06-11T10:59:47Z)
• Architecture for Simulating Behavior Mode Changes in Norm-Aware Autonomous Agents [0.0]
  This paper presents an architecture for simulating the actions of a norm-aware intelligent agent. Updating an agent's behavior mode from a norm-abiding to a riskier one may be relevant when the agent is involved in time-sensitive rescue operations.
  arXiv  Detail & Related papers  (2025-02-13T11:49:02Z)
• Formal Ethical Obligations in Reinforcement Learning Agents: Verification and Policy Updates [0.0]
  Designers need tools to automatically reason about what agents ought to do, how that conflicts with what is actually happening, and how a policy might be modified to remove the conflict. We propose a new deontic logic, Expected Act Utilitarian deontic logic, for enabling this reasoning at design time. Unlike approaches that work at the reward level, working at the logical level increases the transparency of the trade-offs.
  arXiv  Detail & Related papers  (2024-07-31T20:21:15Z)
• Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints [52.37099916582462]
  In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. We propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN) PMN responds appropriately to varying degrees of constraint violations, enabling efficient constraint satisfaction and safe exploration.
  arXiv  Detail & Related papers  (2024-07-22T10:57:32Z)
• Ask-before-Plan: Proactive Language Agents for Real-World Planning [68.08024918064503]
  Proactive Agent Planning requires language agents to predict clarification needs based on user-agent conversation and agent-environment interaction. We propose a novel multi-agent framework, Clarification-Execution-Planning (textttCEP), which consists of three agents specialized in clarification, execution, and planning.
  arXiv  Detail & Related papers  (2024-06-18T14:07:28Z)
• Anticipating Responsibility in Multiagent Planning [9.686474898346392]
  Responsibility anticipation is a process of determining if the actions of an individual agent may cause it to be responsible for a particular outcome. This can be used in a multi-agent planning setting to allow agents to anticipate responsibility in the plans they consider.
  arXiv  Detail & Related papers  (2023-07-31T13:58:49Z)
• Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents' Beliefs about Plans [13.637799815698559]
  We provide an online execution algorithm based on Monte Carlo Tree Search for the agent to plan its action. We show that our agent is better equipped to work in teams without the guarantee of a shared mental model.
  arXiv  Detail & Related papers  (2023-07-07T03:05:34Z)
• Explainable Abuse Detection as Intent Classification and Slot Filling [66.80201541759409]
  We introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.
  arXiv  Detail & Related papers  (2022-10-06T03:33:30Z)
• Formalizing the Problem of Side Effect Regularization [81.97441214404247]
  We propose a formal criterion for side effect regularization via the assistance game framework. In these games, the agent solves a partially observable Markov decision process. We show that this POMDP is solved by trading off the proxy reward with the agent's ability to achieve a range of future tasks.
  arXiv  Detail & Related papers  (2022-06-23T16:36:13Z)
• Reinforcement Learning Guided by Provable Normative Compliance [0.0]
  Reinforcement learning (RL) has shown promise as a tool for engineering safe, ethical, or legal behaviour in autonomous agents. We use multi-objective RL (MORL) to balance the ethical objective of avoiding violations with a non-ethical objective. We show that our approach works for a multiplicity of MORL techniques, and show that it is effective regardless of the magnitude of the punishment we assign.
  arXiv  Detail & Related papers  (2022-03-30T13:10:55Z)
• Policy Regularization for Legible Behavior [0.0]
  In Reinforcement Learning interpretability generally means to provide insight into the agent's mechanisms. This paper borrows from the Explainable Planning literature methods that focus on the legibility of the agent. In our formulation, the decision boundary introduced by legibility impacts the states in which the agent's policy returns an action that has high likelihood also in other policies.
  arXiv  Detail & Related papers  (2022-03-08T10:55:46Z)
• Interpretable Reinforcement Learning with Multilevel Subgoal Discovery [77.34726150561087]
  We propose a novel Reinforcement Learning model for discrete environments. In the model, an agent learns information about environment in the form of probabilistic rules. No reward function is required for learning; an agent only needs to be given a primary goal to achieve.
  arXiv  Detail & Related papers  (2022-02-15T14:04:44Z)
• Modelling Multi-Agent Epistemic Planning in ASP [66.76082318001976]
  This paper presents an implementation of a multi-shot Answer Set Programming-based planner that can reason in multi-agent epistemic settings. The paper shows how the planner, exploiting an ad-hoc epistemic state representation and the efficiency of ASP solvers, has competitive performance results on benchmarks collected from the literature.
  arXiv  Detail & Related papers  (2020-08-07T06:35:56Z)

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.