AI AGENT DECISION PLATFORM WITH DEONTIC REASONING
A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches. The invention uses hierarchical and fuzzy deontic logic implementations alongside connectionist AI/ML to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration. In at least one embodiment, the invention operates through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining coherence, consistency and supporting compound workflows. The invention provides a framework for AI systems to make logically consistent, ethically-aware decisions by combining deontic reasoning with multi-agent coordination, token space communications and knowledge, including on intermediate results, enabling automated decision-making for a variety of applications.
1 . A computing system for an AI agent decision platform with deontic reasoning, the computing system comprising:
one or more hardware processors configured for:
receiving a plurality of tasks at a network of specialized agents, wherein each agent comprises domain-specific knowledge and is bound by deontic constraints comprising at least one of either obligations, permissions, or prohibitions stored in knowledge graphs;
forwarding the plurality of tasks to a centralized distributed graph-based system;
analyzing the tasks using a deontic reasoning subsystem to evaluate compliance with the stored deontic constraints;
generating a plurality of compute graphs that represent the plurality of subtasks;
decomposing compliant tasks into subtasks based on agent domain expertise and associated deontic constraints;
generating compute graphs that represent the subtasks with their associated deontic constraints;
distributing the compute graphs to agents within the network based on the agents' domain expertise and deontic permissions; and
executing the subtasks while maintaining compliance with the stored deontic constraints.
2 . The computing system of claim 1 , wherein agents may be human or non-human agents.
3 . The computing system of claim 1 , wherein agents receive feedback and adjust task allocation based on the feedback.
4 . The computing system of claim 1 , wherein knowledge graphs are updated based on a plurality of contextual data and sensor data.
5 . The computing system of claim 4 , wherein sensor data includes but is not limited to Internet of Things (IoT) data, medical device data, wearable device data, video data, and image data.
6 . The computing system of claim 1 , wherein the system further comprises a resource-ethical optimization module that applies multi-objective optimization across both computational cost metrics and compliance metrics derived from deontic constraints, thereby dynamically allocating tasks to nodes or agents that best satisfy performance objectives while minimizing ethical risk.
7 . The computing system of claim 1 , wherein the system further comprises a dynamic deontic breaker subsystem configured to:
monitor deontic risk scores during runtime execution of federated distributed computational graph pipelines;
automatically inject circuit breaker nodes when a calculated risk score exceeds a predefined threshold;
halt or redirect task processing upon circuit breaker activation; and
require either human agent authorization or implementation of additional anonymization measures before allowing pipeline resumption.
8 . The computing system of claim 7 , wherein the dynamic deontic circuit breaker subsystem integrates with a multi-objective resource allocation and load-balancing framework to:
preemptively escalate high-risk tasks for human review;
redirect tasks to nodes with enhanced security capabilities; or
automatically terminate task execution when no compliant resource allocation path exists.
9 . The computing system of claim 1 , wherein the system applies privacy-preserving transformations to raw data before federation node assignment through:
implementing a least one of data anonymization, encryption, homomorphic encryption, tokenization, and differential privacy;
maintaining deontic privacy constraint compliance throughout data processing; and
enabling partial or blind execution capabilities across the federation network.
10 . A computer-implemented method for an AI agent decision platform with deontic reasoning, the computer-implemented method comprising the steps of:
receiving a plurality of tasks at a network of specialized agents, wherein each agent comprises domain-specific knowledge and is bound by deontic constraints comprising at least one of either obligations, permissions, or prohibitions stored in knowledge graphs;
forwarding the plurality of tasks to a centralized distributed graph-based system;
analyzing the tasks using a deontic reasoning subsystem to evaluate compliance with the stored deontic constraints;
generating a plurality of compute graphs that represent the plurality of subtasks;
decomposing compliant tasks into subtasks based on agent domain expertise and associated deontic constraints;
generating compute graphs that represent the subtasks with their associated deontic constraints;
distributing the compute graphs to agents within the network based on the agents' domain expertise and deontic permissions; and
executing the subtasks while maintaining compliance with the stored deontic constraints.
11 . The method of claim 10 , wherein agents may be human or non-human agents.
12 . The method of claim 10 , wherein agents receive feedback and adjust task allocation based on the feedback.
13 . The method of claim 10 , wherein knowledge graphs are updated based on a plurality of contextual data and sensor data.
14 . The method of claim 13 , wherein sensor data includes but is not limited to Internet of Things (IoT) data, medical device data, wearable device data, video data, and image data.
15 . The method of claim 10 , wherein the method further comprises applying multi-objective optimization across both computational cost metrics and compliance metrics derived from deontic constraints using a resource-ethical optimization module, thereby dynamically allocating tasks to nodes or agents that best satisfy performance objectives while minimizing ethical risk.
16 . The method of claim 10 , wherein the method further comprises a dynamic deontic breaker subsystem configured to:
monitor deontic risk scores during runtime execution of federated distributed computational graph pipelines;
automatically inject circuit breaker nodes when a calculated risk score exceeds a predefined threshold;
halt or redirect task processing upon circuit breaker activation; and
require either human agent authorization or implementation of additional anonymization measures before allowing pipeline resumption.
17 . The method of claim 16 , wherein the dynamic deontic circuit breaker subsystem integrates with a multi-objective resource allocation and load-balancing framework to:
preemptively escalate high-risk tasks for human review;
redirect tasks to nodes with enhanced security capabilities; or
automatically terminate task execution when no compliant resource allocation path exists.
18 . The method of claim 10 , wherein the method applies privacy-preserving transformations to raw data before federation node assignment through:
implementing a least one of data anonymization, encryption, homomorphic encryption, tokenization, and differential privacy;
maintaining deontic privacy constraint compliance throughout data processing; and
enabling partial or blind execution capabilities across the federation network.
19 . A system for an AI agent decision platform with deontic reasoning, comprising one or more computers with executable instructions that, when executed, cause the system to:
receive a plurality of tasks at a network of specialized agents, wherein each agent comprises domain-specific knowledge and is bound by deontic constraints comprising at least one of either obligations, permissions, or prohibitions stored in knowledge graphs;
forward the plurality of tasks to a centralized distributed graph-based system;
analyze the tasks using a deontic reasoning subsystem to evaluate compliance with the stored deontic constraints;
generate a plurality of compute graphs that represent the plurality of subtasks;
decompose compliant tasks into subtasks based on agent domain expertise and associated deontic constraints;
generate compute graphs that represent the subtasks with their associated deontic constraints;
distribute the compute graphs to agents within the network based on the agents' domain expertise and deontic permissions; and
execute the subtasks while maintaining compliance with the stored deontic constraints.
20 . The system of claim 19 , wherein agents may be human or non-human agents.
21 . The system of claim 19 , wherein agents receive feedback and adjust task allocation based on the feedback.
22 . The system of claim 19 , wherein knowledge graphs are updated based on a plurality of contextual data and sensor data.
23 . The system of claim 22 , wherein sensor data includes but is not limited to Internet of Things (IoT) data, medical device data, wearable device data, video data, and image data.
24 . The system of claim 19 , wherein the system further comprises a resource-ethical optimization module that applies multi-objective optimization across both computational cost metrics and compliance metrics derived from deontic constraints, thereby dynamically allocating tasks to nodes or agents that best satisfy performance objectives while minimizing ethical risk.
25 . The system of claim 19 , wherein the system further comprises a dynamic deontic breaker subsystem configured to:
monitor deontic risk scores during runtime execution of federated distributed computational graph pipelines;
automatically inject circuit breaker nodes when a calculated risk score exceeds a predefined threshold;
halt or redirect task processing upon circuit breaker activation; and
require either human agent authorization or implementation of additional anonymization measures before allowing pipeline resumption.
26 . The system of claim 25 , wherein the dynamic deontic circuit breaker subsystem integrates with a multi-objective resource allocation and load-balancing framework to:
preemptively escalate high-risk tasks for human review;
redirect tasks to nodes with enhanced security capabilities; or
automatically terminate task execution when no compliant resource allocation path exists.
27 . The system of claim 19 , wherein the system applies privacy-preserving transformations to raw data before federation node assignment through:
implementing a least one of data anonymization, encryption, homomorphic encryption, tokenization, and differential privacy;
maintaining deontic privacy constraint compliance throughout data processing; and
enabling partial or blind execution capabilities across the federation network.