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Formalizing Value-based Actions and Consequentialist Ethics


Core Concepts
Agents act based on their values, formalized through Value-based Formal Reasoning (VFR) in a computational framework for consequentialist ethics.
Abstract
The content introduces a formalization of value-based actions and consequentialist ethics using the STRIPS formalization. It discusses how agents' behaviors are guided by personal or institutional values, emphasizing the importance of motivated reasoning in decision-making processes. The paper proposes an action framework based on VFR to express actions that align with an agent's value profile. It also explores the concept of satisficing consequentialism, pluralistic values, and act-based ethics. The implementation of the model in PROLOG is discussed along with related work and future directions. Directory: Introduction Agents' behavior guided by various factors like beliefs, desires, intentions. Importance of motivated reasoning in selecting propositions. Consequentialism The foundation of modern consequentialism by Jeremy Bentham. Introduction to satisficing consequentialism as a moral permissibility concept. Agents, Propositions, Values, and Weights Value-based language for decision making outlined. Functions to assess propositions with respect to values introduced. State Transitions Introduction to STRIPS formalization for representing state transitions. Implementation Experimental implementation in PROLOG discussed with code fragments. Related Work and Discussion Comparison with existing approaches to ethical decision-making and consequentialist ethics.
Stats
"AgentValueToWeight = (Agent × Value) →Weight" "AgentValuePropWeight: (Agent × Value × Prop) →Weight" "Definition 1 (propBaseClean) propBaseClean is of type Prop." "Definition 2 Let S be a set of states." "Definition 3 (Goals Relative to Agent’s propBaseClean)" "Definition 4 (Agent’s Actions Introducing Propositions)" "Definition 5 (Revised Transition Function)" "Definition 6 (Agent’s Actions Removing Bad Things)"
Quotes
"An individualized value profile of the agent corresponds to the preferential nature of the ethical approach we have adopted." "Our model allows for a multi-agent approach to behaviour which reflects value choices."

Deeper Inquiries

How can this model be adapted to incorporate multiple agents' interactions?

In order to adapt this model to incorporate multiple agents' interactions, we would need to extend the framework to account for the values and value profiles of each individual agent. Each agent would have their own set of values, thresholds, and acceptable propositions based on their unique value profile. The actions taken by an agent should align with their specific value preferences and contribute towards achieving states that are compatible with their individual propBaseClean. To facilitate interactions between multiple agents, we could introduce mechanisms for negotiation, cooperation, or conflict resolution based on the compatibility of their respective propBaseClean sets. Agents may collaborate on actions that lead to outcomes satisfying all parties' values or engage in negotiations where compromises are made based on shared propositions from each agent's propBaseClean. This adaptation would enable a more dynamic and realistic representation of ethical decision-making within multi-agent systems.

What are the implications of excluding immoral consequences from plans?

Excluding immoral consequences from plans has significant ethical implications as it ensures that actions taken by an agent do not result in outcomes that contradict its established values. By filtering out propositions incompatible with an agent's value profile (propBaseClean), the model promotes decision-making aligned with moral principles and personal ethics. One implication is that it helps maintain consistency between an agent's intentions and its actual behavior in achieving desired states. This exclusion mechanism prevents agents from inadvertently engaging in actions that go against their core values or ethical standards, thereby promoting integrity and coherence in decision-making processes. Moreover, by excluding immoral consequences from plans, the model upholds a form of consequentialist ethics focused on satisficing outcomes consistent with an individual's value preferences rather than maximizing utility at any cost. It emphasizes prioritizing morally acceptable results over purely optimizing objectives without regard for ethical considerations.

How might this approach impact reinforcement learning systems integrating ethics?

Integrating this approach into reinforcement learning systems can enhance the ethical considerations governing AI decision-making processes. By incorporating an Agent’s value profile into action selection criteria through mechanisms like propBaseClean evaluation, reinforcement learning algorithms can prioritize choices leading to morally sound outcomes according to predefined ethical guidelines. This integration could lead to more responsible AI behavior by ensuring that machine decisions align with human-defined values and moral standards encoded within the system. Reinforcement learning models equipped with such ethical filters can avoid harmful or unethical actions even when they promise short-term gains or rewards typically sought after during training phases. Furthermore, implementing these ethically informed constraints can foster trustworthiness in AI applications by demonstrating a commitment to principled conduct guided by specified moral frameworks rather than solely focusing on performance metrics without considering broader societal impacts or normative considerations.
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