Methods and systems for dynamic risks countermeasures in complex networked systems

1 . A method for dynamically generating countermeasures for complex networked systems, the method comprising:

in an initialization phase, in response to receiving a user query at a large language model (LLM), generating AI agents each defined by a task derived from the user query, wherein each AI agent autonomously or semi-autonomously interacts with an environment by performing steps comprising:

in response to receiving a network and a user input, parsing data related to entities and relationships in the network to define nodes and edges of a graph representing the network;

transforming a model of the network to a Markov random field (MRF) format;

applying graph representation learning to obtain embeddings that capture structural and relational information within the network;

assigning to choice nodes, which represent decision points in the network, random variables that represent decision options associated with the decision points;

assigning to decision nodes, which represent outcomes of the decision options, random variables that represent possible outcomes or probabilities of the possible outcomes;

for the nodes and edges, defining potential functions, which quantify a desirability or utility of different configurations of one or more random variables and reflect preferences or objectives in a decision-making process;

in response to receiving new data, updating the potential functions to enable the graph to adapt to a change in a network of systems;

using the embeddings to predict decision outcomes and define updated potential functions;

in an inference operation, performing steps comprising:

using the at least some of the choice nodes, decision nodes, random variables, or potential functions to determine an optimal decision;

finding a configuration of random variables that maximizes a utility or minimizes a cost;

calculating an expected utility of chance nodes in the MRF to assess an impact of probabilistic events on a performance of the network;

selecting candidate subgraphs from the network, wherein at least one AI agent computes a marginal distribution at each node and applying an objective inference to each subgraph; and

based on the user query, generating parameters and applying a multi-objective optimization process to the candidate subgraphs to select a subgraph; and

using the LLM to output a result based on the selected subgraph.

2 . The method of claim 1 , further comprising performing at least one or a what-if risk analysis or a risk condition simulation.

3 . The method of claim 1 , further comprising fine-tuning a model of the network that represents a physical network and using the network to customize an interpreter based on system-specific documents and data sources.

4 . The method of claim 3 , further comprising using sensor data to update the network model.

5 . The method of claim 1 , further comprising using at least one of a graph pruning technique to reduce a complexity of one or more graphs, a restoration optimizing technique, or a community resilience technique.

6 . The method of claim 1 , further comprising using one or more predefined task-based AI agents to automatically construct a joint probability distribution over the random variables in the network, the joint probability distribution being time-dependent and reflecting a dynamic network condition.

7 . The method of claim 1 , wherein the parameters comprise at least one of a cost or a time associated with inventory levels at different locations, the parameters being based on current demand estimates.

8 . The method of claim 1 , wherein the initialization phase further comprises at least one of validating a task completion and logging execution steps.

9 . The method of claim 1 , further comprising generating a set of recovery objectives associated with at least one of a cost, a time, or a system-based parameter.

10 . The method of claim 1 , wherein the embeddings capture temporal changes in the network.

11 . The method of claim 1 , wherein the potential functions are associated with both deterministic and probabilistic risk factors.

12 . The method of claim 1 , further comprising using an objective optimization process to select an optimal solution from a multi-objective optimization.

13 . The method of claim 1 , wherein the AI agent uses historical data to assign probabilities to random variables.

14 . The method of claim 1 , further comprising updating the graph based on the new data to enable continuous learning and adaptation.

15 . The method of claim 1 , further comprising integrating with a domain-specific system to enable a real-time data update.

16 . The method of claim 1 , wherein the system generates a candidate recovery network for at least one of a deterministic risk scenario or a probabilistic risk scenario.

17 . The method according to claim 1 , wherein the MRF is defined as:

ψ c ( x c )=exp(−αCost( x c )−β·Unmetdemand( x c ))−γExcessive Inventory( x c )

wherein x c is a set of random variables in clique c, representing inventory levels datacenters; Cost (x c ) represents a transportation cost associated with a current configuration of inventory levels; Unmetdemand(x c ) represents a penalty for unmet demand at each datacenter; Excessive Inventory(x c ) represents a penalty for excess inventory that exceeds storage capacity or incurs additional holding costs; and α, β, γ are weight parameters that balance an importance of each term in the potential function.

18 . A non-transitory computer-readable medium for storing instructions for executing a process, the instructions comprising:

in an initialization phase, in response to receiving a user query at a large language model (LLM), generating AI agents each defined by a task derived from the user query, wherein each AI agent autonomously or semi-autonomously interacts with an environment by performing steps comprising:

in response to receiving a network and a user input, parsing data related to entities and relationships in the network to define nodes and edges of a graph representing the network;

transforming a model of the network to a Markov random field (MRF) format;

applying graph representation learning to obtain embeddings that capture structural and relational information within the network;

assigning to choice nodes, which represent decision points in the network, random variables that represent decision options associated with the decision points;

assigning to decision nodes, which represent outcomes of the decision options, random variables that represent possible outcomes or probabilities of the possible outcomes;

for the nodes and edges, defining potential functions, which quantify a desirability or utility of different configurations of one or more random variables and reflect preferences or objectives in a decision-making process;

in response to receiving new data, updating the potential functions to enable the graph to adapt to a change in a network of systems;

using the embeddings to predict decision outcomes and define updated potential functions;

in an inference operation, performing steps comprising:

using the at least some of the choice nodes, decision nodes, random variables, or potential functions to determine an optimal decision;

finding a configuration of random variables that maximizes a utility or minimizes a cost;

calculating an expected utility of chance nodes in the MRF to assess an impact of probabilistic events on a performance of the network;

selecting candidate subgraphs from the network, wherein at least one AI agent computes a marginal distribution at each node and applying an objective inference to each subgraph; and

based on the user query, generating parameters and applying a multi-objective optimization process to the candidate subgraphs to select a subgraph; and

using the LLM to output a result based on the selected subgraph.

19 . An apparatus, comprising:

one or more processors, configured to:

in an initialization phase, in response to receiving a user query at a large language model (LLM), generate AI agents each defined by a task derived from the user query, wherein each AI agent autonomously or semi-autonomously interacts with an environment and performs steps comprising:

in response to receiving a network and a user input, parsing data related to entities and relationships in the network to define nodes and edges of a graph representing the network;

transforming a model of the network to a Markov random field (MRF) format;

applying graph representation learning to obtain embeddings that capture structural and relational information within the network;

assigning to choice nodes, which represent decision points in the network, random variables that represent decision options associated with the decision points;

assigning to decision nodes, which represent outcomes of the decision options, random variables that represent possible outcomes or probabilities of the possible outcomes;

for the nodes and edges, defining potential functions, which quantify a desirability or utility of different configurations of one or more random variables and reflect preferences or objectives in a decision-making process;

in response to receiving new data, updating the potential functions to enable the graph to adapt to a change in a network of systems;

using the embeddings to predict decision outcomes and define updated potential functions; and

in an inference operation, perform steps comprising:

using the at least some of the choice nodes, decision nodes, random variables, or potential functions to determine an optimal decision;

finding a configuration of random variables that maximizes a utility or minimizes a cost;

calculating an expected utility of chance nodes in the MRF to assess an impact of probabilistic events on a performance of the network;

selecting candidate subgraphs from the network, wherein at least one AI agent computes a marginal distribution at each node and applying an objective inference to each subgraph; and

based on the user query, generating parameters and applying a multi-objective optimization process to the candidate subgraphs to select a subgraph; and

using the LLM to output a result based on the selected subgraph.