agents

%% distributed_consensus_engine.erl
%% Advanced distributed consensus mechanisms for agent decision-making
%% Implements Raft, Byzantine fault tolerance, and novel consensus algorithms
-module(distributed_consensus_engine).
-behaviour(gen_server).

-export([
    start_link/0,
    propose_consensus/3,
    join_consensus_cluster/2,
    leave_consensus_cluster/1,
    byzantine_fault_tolerant_consensus/3,
    quantum_consensus_protocol/3,
    hierarchical_consensus/4,
    async_consensus_with_eventual_consistency/3,
    consensus_with_machine_learning/3,
    federated_byzantine_agreement/4,
    probabilistic_consensus/4
]).

-export([init/1, handle_call/3, handle_cast/2, handle_info/2, terminate/2, code_change/3]).

-define(CONSENSUS_NODES, consensus_cluster_nodes).
-define(CONSENSUS_LOG, consensus_decision_log).
-define(BYZANTINE_STATE, byzantine_consensus_state).

-record(state, {
    node_id,
    cluster_nodes = #{},
    consensus_protocols = #{},
    current_term = 0,
    voted_for = undefined,
    log = [],
    commit_index = 0,
    last_applied = 0,
    leader_id = undefined,
    election_timeout,
    heartbeat_interval = 150,
    byzantine_tolerance = 0,
    quantum_entangled_nodes = [],
    consensus_history = [],
    machine_learning_models = #{},
    network_partition_detector,
    consensus_performance_metrics = #{}
}).

-record(consensus_proposal, {
    id,
    proposer_id,
    proposal_type, % value, configuration, membership
    value,
    term,
    timestamp,
    required_confirmations,
    timeout,
    priority = normal,
    byzantine_proof = undefined,
    quantum_signature = undefined
}).

-record(raft_log_entry, {
    index,
    term,
    command,
    timestamp,
    client_id,
    sequence_num
}).

-record(byzantine_vote, {
    voter_id,
    proposal_id,
    vote, % accept, reject, abstain
    proof_of_work,
    digital_signature,
    timestamp,
    byzantine_round
}).

-record(consensus_result, {
    proposal_id,
    decision, % accepted, rejected, timeout
    final_value,
    participating_nodes,
    consensus_achieved_at,
    proof_of_consensus,
    byzantine_safety_guaranteed,
    consensus_algorithm_used
}).

%% Public API

start_link() ->
    gen_server:start_link({local, ?MODULE}, ?MODULE, [], []).

%% Propose a value for consensus with configurable algorithm
propose_consensus(Value, Algorithm, Options) ->
    gen_server:call(?MODULE, {propose_consensus, Value, Algorithm, Options}, 30000).

%% Join consensus cluster as a new node
join_consensus_cluster(ClusterNodes, NodeCapabilities) ->
    gen_server:call(?MODULE, {join_cluster, ClusterNodes, NodeCapabilities}).

%% Leave consensus cluster gracefully
leave_consensus_cluster(Reason) ->
    gen_server:call(?MODULE, {leave_cluster, Reason}).

%% Byzantine fault-tolerant consensus (3f+1 nodes to tolerate f faults)
byzantine_fault_tolerant_consensus(Value, FaultTolerance, SecurityLevel) ->
    gen_server:call(?MODULE, {byzantine_consensus, Value, FaultTolerance, SecurityLevel}, 60000).

%% Quantum-inspired consensus with entangled decision nodes
quantum_consensus_protocol(Value, QuantumNodes, EntanglementStrength) ->
    gen_server:call(?MODULE, {quantum_consensus, Value, QuantumNodes, EntanglementStrength}, 45000).

%% Hierarchical consensus with multiple decision layers
hierarchical_consensus(Value, Hierarchy, DelegationRules, ConflictResolution) ->
    gen_server:call(?MODULE, {hierarchical_consensus, Value, Hierarchy, DelegationRules, ConflictResolution}).

%% Asynchronous consensus with eventual consistency guarantees
async_consensus_with_eventual_consistency(Value, ConsistencyLevel, ConvergenceTimeout) ->
    gen_server:call(?MODULE, {async_consensus, Value, ConsistencyLevel, ConvergenceTimeout}).

%% Machine learning-enhanced consensus prediction and optimization
consensus_with_machine_learning(Value, LearningModel, PredictionContext) ->
    gen_server:call(?MODULE, {ml_consensus, Value, LearningModel, PredictionContext}).

%% Federated Byzantine Agreement for multi-cluster consensus
federated_byzantine_agreement(Value, FederatedClusters, TrustMatrix, QuorumSlices) ->
    gen_server:call(?MODULE, {federated_consensus, Value, FederatedClusters, TrustMatrix, QuorumSlices}).

%% Probabilistic consensus with confidence intervals
probabilistic_consensus(Value, ConfidenceLevel, ProbabilisticModel, UncertaintyTolerance) ->
    gen_server:call(?MODULE, {probabilistic_consensus, Value, ConfidenceLevel, ProbabilisticModel, UncertaintyTolerance}).

%% Gen_server callbacks

init([]) ->
    % Initialize consensus node
    NodeId = generate_node_id(),
    
    % Create ETS tables for consensus state
    ets:new(?CONSENSUS_NODES, [named_table, public, set]),
    ets:new(?CONSENSUS_LOG, [named_table, public, ordered_set]),
    ets:new(?BYZANTINE_STATE, [named_table, public, set]),
    
    % Start network partition detector
    PartitionDetector = spawn_link(fun() -> network_partition_monitor() end),
    
    % Initialize machine learning models for consensus optimization
    MLModels = initialize_consensus_ml_models(),
    
    % Start election timeout timer
    ElectionTimeout = generate_election_timeout(),
    schedule_election_timeout(ElectionTimeout),
    
    {ok, #state{
        node_id = NodeId,
        election_timeout = ElectionTimeout,
        network_partition_detector = PartitionDetector,
        machine_learning_models = MLModels
    }}.

handle_call({propose_consensus, Value, Algorithm, Options}, From, State) ->
    ProposalId = generate_proposal_id(),
    
    % Create consensus proposal
    Proposal = #consensus_proposal{
        id = ProposalId,
        proposer_id = State#state.node_id,
        proposal_type = value,
        value = Value,
        term = State#state.current_term,
        timestamp = erlang:system_time(microsecond),
        required_confirmations = calculate_required_confirmations(Algorithm, Options),
        timeout = maps:get(timeout, Options, 30000)
    },
    
    % Execute consensus algorithm
    spawn_link(fun() ->
        Result = execute_consensus_algorithm(Algorithm, Proposal, Options, State),
        gen_server:reply(From, Result)
    end),
    
    {noreply, State};

handle_call({join_cluster, ClusterNodes, Capabilities}, _From, State) ->
    Result = join_consensus_cluster_internal(ClusterNodes, Capabilities, State),
    {reply, Result, State};

handle_call({leave_cluster, Reason}, _From, State) ->
    Result = leave_consensus_cluster_internal(Reason, State),
    {reply, Result, State};

handle_call({byzantine_consensus, Value, FaultTolerance, SecurityLevel}, From, State) ->
    spawn_link(fun() ->
        Result = execute_byzantine_consensus(Value, FaultTolerance, SecurityLevel, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({quantum_consensus, Value, QuantumNodes, Strength}, From, State) ->
    spawn_link(fun() ->
        Result = execute_quantum_consensus(Value, QuantumNodes, Strength, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({hierarchical_consensus, Value, Hierarchy, Rules, Resolution}, From, State) ->
    spawn_link(fun() ->
        Result = execute_hierarchical_consensus(Value, Hierarchy, Rules, Resolution, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({async_consensus, Value, Consistency, Timeout}, From, State) ->
    spawn_link(fun() ->
        Result = execute_async_consensus(Value, Consistency, Timeout, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({ml_consensus, Value, Model, Context}, From, State) ->
    spawn_link(fun() ->
        Result = execute_ml_consensus(Value, Model, Context, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({federated_consensus, Value, Clusters, Trust, Quorums}, From, State) ->
    spawn_link(fun() ->
        Result = execute_federated_consensus(Value, Clusters, Trust, Quorums, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call({probabilistic_consensus, Value, Confidence, Model, Tolerance}, From, State) ->
    spawn_link(fun() ->
        Result = execute_probabilistic_consensus(Value, Confidence, Model, Tolerance, State),
        gen_server:reply(From, Result)
    end),
    {noreply, State};

handle_call(_Request, _From, State) ->
    {reply, {error, unknown_request}, State}.

handle_cast({vote_request, Term, CandidateId, LastLogIndex, LastLogTerm}, State) ->
    NewState = handle_vote_request(Term, CandidateId, LastLogIndex, LastLogTerm, State),
    {noreply, NewState};

handle_cast({vote_response, Term, VoteGranted, VoterId}, State) ->
    NewState = handle_vote_response(Term, VoteGranted, VoterId, State),
    {noreply, NewState};

handle_cast({append_entries, Term, LeaderId, PrevLogIndex, PrevLogTerm, Entries, LeaderCommit}, State) ->
    NewState = handle_append_entries(Term, LeaderId, PrevLogIndex, PrevLogTerm, Entries, LeaderCommit, State),
    {noreply, NewState};

handle_cast({byzantine_vote, Vote}, State) ->
    NewState = handle_byzantine_vote(Vote, State),
    {noreply, NewState};

handle_cast({quantum_entanglement_update, Nodes, EntanglementState}, State) ->
    NewState = State#state{quantum_entangled_nodes = Nodes},
    update_quantum_consensus_state(EntanglementState),
    {noreply, NewState};

handle_cast(_Msg, State) ->
    {noreply, State}.

handle_info({election_timeout}, State) ->
    NewState = start_leader_election(State),
    {noreply, NewState};

handle_info({heartbeat_timeout}, State) ->
    NewState = send_heartbeat_to_followers(State),
    schedule_heartbeat_timeout(State#state.heartbeat_interval),
    {noreply, NewState};

handle_info({consensus_proposal_timeout, ProposalId}, State) ->
    handle_proposal_timeout(ProposalId),
    {noreply, State};

handle_info({network_partition_detected, PartitionInfo}, State) ->
    NewState = handle_network_partition(PartitionInfo, State),
    {noreply, NewState};

handle_info({consensus_performance_update, Metrics}, State) ->
    NewState = update_consensus_performance_metrics(Metrics, State),
    {noreply, NewState};

handle_info(_Info, State) ->
    {noreply, State}.

terminate(_Reason, _State) ->
    ok.

code_change(_OldVsn, State, _Extra) ->
    {ok, State}.

%% Consensus Algorithm Implementations

execute_consensus_algorithm(raft, Proposal, Options, State) ->
    execute_raft_consensus(Proposal, Options, State);
execute_consensus_algorithm(pbft, Proposal, Options, State) ->
    execute_pbft_consensus(Proposal, Options, State);
execute_consensus_algorithm(tendermint, Proposal, Options, State) ->
    execute_tendermint_consensus(Proposal, Options, State);
execute_consensus_algorithm(avalanche, Proposal, Options, State) ->
    execute_avalanche_consensus(Proposal, Options, State);
execute_consensus_algorithm(stellar_consensus, Proposal, Options, State) ->
    execute_stellar_consensus_protocol(Proposal, Options, State);
execute_consensus_algorithm(hashgraph, Proposal, Options, State) ->
    execute_hashgraph_consensus(Proposal, Options, State).

execute_raft_consensus(Proposal, Options, State) ->
    % Enhanced Raft implementation with optimizations
    
    % Phase 1: Leader election if needed
    {ok, LeaderState} = ensure_leader_elected(State),
    
    % Phase 2: Log replication
    LogEntry = create_raft_log_entry(Proposal, LeaderState),
    ReplicationResult = replicate_log_entry(LogEntry, LeaderState),
    
    % Phase 3: Commitment
    case ReplicationResult of
        {ok, MajorityAchieved} when MajorityAchieved ->
            CommitResult = commit_log_entry(LogEntry, LeaderState),
            
            % Phase 4: Apply to state machine
            ApplicationResult = apply_to_state_machine(LogEntry, LeaderState),
            
            create_consensus_result(Proposal, accepted, ApplicationResult);
        {error, Reason} ->
            create_consensus_result(Proposal, rejected, Reason)
    end.

execute_byzantine_consensus(Value, FaultTolerance, SecurityLevel, State) ->
    % Practical Byzantine Fault Tolerance (pBFT) with enhancements
    
    % Calculate required nodes (3f + 1 for f Byzantine faults)
    RequiredNodes = 3 * FaultTolerance + 1,
    AvailableNodes = get_available_consensus_nodes(),
    
    case length(AvailableNodes) >= RequiredNodes of
        true ->
            % Phase 1: Pre-prepare
            PreparePhase = execute_byzantine_prepare_phase(Value, AvailableNodes, SecurityLevel),
            
            % Phase 2: Prepare
            case PreparePhase of
                {ok, PrepareQuorum} ->
                    CommitPhase = execute_byzantine_commit_phase(Value, PrepareQuorum, SecurityLevel),
                    
                    % Phase 3: Commit
                    case CommitPhase of
                        {ok, CommitQuorum} ->
                            FinalResult = finalize_byzantine_consensus(Value, CommitQuorum),
                            
                            % Verify Byzantine safety and liveness
                            SafetyProof = verify_byzantine_safety(FinalResult, FaultTolerance),
                            
                            #consensus_result{
                                proposal_id = generate_proposal_id(),
                                decision = accepted,
                                final_value = Value,
                                participating_nodes = CommitQuorum,
                                consensus_achieved_at = erlang:system_time(microsecond),
                                proof_of_consensus = SafetyProof,
                                byzantine_safety_guaranteed = true,
                                consensus_algorithm_used = pbft
                            };
                        {error, CommitError} ->
                            create_error_result(byzantine_commit_failed, CommitError)
                    end;
                {error, PrepareError} ->
                    create_error_result(byzantine_prepare_failed, PrepareError)
            end;
        false ->
            create_error_result(insufficient_nodes, 
                #{required => RequiredNodes, available => length(AvailableNodes)})
    end.

execute_quantum_consensus(Value, QuantumNodes, EntanglementStrength, State) ->
    % Quantum-inspired consensus using entangled decision states
    
    % Phase 1: Establish quantum entanglement between nodes
    EntanglementResult = establish_quantum_entanglement_network(QuantumNodes, EntanglementStrength),
    
    case EntanglementResult of
        {ok, EntangledNetwork} ->
            % Phase 2: Create superposition of consensus states
            SuperpositionStates = create_consensus_superposition(Value, EntangledNetwork),
            
            % Phase 3: Apply quantum interference for convergence
            InterferenceResult = apply_quantum_interference_for_consensus(SuperpositionStates),
            
            % Phase 4: Measure consensus outcome
            {MeasurementResult, CollapsedState} = quantum_measurement_consensus(InterferenceResult),
            
            % Phase 5: Verify quantum consensus properties
            QuantumProof = verify_quantum_consensus_properties(CollapsedState, EntangledNetwork),
            
            #consensus_result{
                proposal_id = generate_proposal_id(),
                decision = accepted,
                final_value = MeasurementResult,
                participating_nodes = QuantumNodes,
                consensus_achieved_at = erlang:system_time(microsecond),
                proof_of_consensus = QuantumProof,
                byzantine_safety_guaranteed = false, % Different security model
                consensus_algorithm_used = quantum_consensus
            };
        {error, EntanglementError} ->
            create_error_result(quantum_entanglement_failed, EntanglementError)
    end.

execute_hierarchical_consensus(Value, Hierarchy, DelegationRules, ConflictResolution, State) ->
    % Multi-layer hierarchical consensus
    
    % Phase 1: Bottom-up consensus propagation
    BottomUpResults = execute_bottom_up_consensus(Value, Hierarchy, DelegationRules),
    
    % Phase 2: Conflict detection and resolution
    ConflictAnalysis = detect_hierarchical_conflicts(BottomUpResults),
    
    case ConflictAnalysis of
        {conflicts_detected, Conflicts} ->
            % Apply conflict resolution strategy
            ResolvedResults = apply_conflict_resolution(Conflicts, ConflictResolution),
            
            % Phase 3: Top-down validation
            ValidationResult = validate_hierarchical_consensus(ResolvedResults, Hierarchy),
            
            create_hierarchical_consensus_result(Value, ValidationResult);
        {no_conflicts, Results} ->
            % Direct consensus achieved
            create_hierarchical_consensus_result(Value, Results)
    end.

execute_ml_consensus(Value, LearningModel, PredictionContext, State) ->
    % Machine learning-enhanced consensus
    
    % Phase 1: Predict optimal consensus strategy
    MLModel = maps:get(LearningModel, State#state.machine_learning_models),
    PredictedStrategy = predict_optimal_consensus_strategy(MLModel, Value, PredictionContext),
    
    % Phase 2: Execute predicted strategy
    StrategyResult = execute_predicted_consensus_strategy(PredictedStrategy, Value, State),
    
    % Phase 3: Learn from outcome
    LearningUpdate = update_ml_model_from_outcome(MLModel, PredictedStrategy, StrategyResult),
    
    % Phase 4: Adaptive optimization
    OptimizedResult = apply_adaptive_optimization(StrategyResult, LearningUpdate),
    
    create_ml_consensus_result(Value, OptimizedResult, LearningUpdate).

%% Utility Functions

generate_node_id() ->
    {node(), erlang:system_time(microsecond), rand:uniform(1000000)}.

generate_proposal_id() ->
    list_to_binary([
        atom_to_list(node()), 
        "_", 
        integer_to_list(erlang:system_time(microsecond)), 
        "_", 
        integer_to_list(rand:uniform(1000000))
    ]).

generate_election_timeout() ->
    % Randomized timeout to prevent split votes
    150 + rand:uniform(150). % 150-300ms

schedule_election_timeout(Timeout) ->
    erlang:send_after(Timeout, self(), {election_timeout}).

schedule_heartbeat_timeout(Interval) ->
    erlang:send_after(Interval, self(), {heartbeat_timeout}).

calculate_required_confirmations(Algorithm, Options) ->
    case Algorithm of
        raft -> 
            NodeCount = get_cluster_size(),
            (NodeCount div 2) + 1; % Majority
        pbft ->
            FaultTolerance = maps:get(fault_tolerance, Options, 1),
            2 * FaultTolerance + 1; % 2f + 1 for f faults
        stellar_consensus ->
            maps:get(quorum_threshold, Options, 0.67); % 67% threshold
        _ ->
            maps:get(required_confirmations, Options, 3)
    end.

network_partition_monitor() ->
    % Monitor network partitions and connectivity
    receive
        {check_partition, Nodes} ->
            PartitionStatus = detect_network_partition(Nodes),
            case PartitionStatus of
                {partition_detected, PartitionInfo} ->
                    distributed_consensus_engine ! {network_partition_detected, PartitionInfo};
                no_partition ->
                    ok
            end,
            network_partition_monitor();
        stop_monitor ->
            ok
    after 5000 ->
        perform_periodic_partition_check(),
        network_partition_monitor()
    end.

initialize_consensus_ml_models() ->
    #{
        strategy_predictor => train_consensus_strategy_model(),
        performance_optimizer => train_performance_optimization_model(),
        fault_predictor => train_fault_prediction_model(),
        adaptive_timeout => train_adaptive_timeout_model()
    }.

%% Placeholder implementations for complex consensus operations
join_consensus_cluster_internal(_Nodes, _Capabilities, _State) -> {ok, joined}.
leave_consensus_cluster_internal(_Reason, _State) -> {ok, left}.
handle_vote_request(_Term, _Candidate, _LogIndex, _LogTerm, State) -> State.
handle_vote_response(_Term, _Granted, _Voter, State) -> State.
handle_append_entries(_Term, _Leader, _PrevIndex, _PrevTerm, _Entries, _Commit, State) -> State.
handle_byzantine_vote(_Vote, State) -> State.
update_quantum_consensus_state(_State) -> ok.
start_leader_election(State) -> State.
send_heartbeat_to_followers(State) -> State.
handle_proposal_timeout(_ProposalId) -> ok.
handle_network_partition(_PartitionInfo, State) -> State.
update_consensus_performance_metrics(_Metrics, State) -> State.
ensure_leader_elected(State) -> {ok, State}.
create_raft_log_entry(_Proposal, _State) -> #raft_log_entry{}.
replicate_log_entry(_Entry, _State) -> {ok, true}.
commit_log_entry(_Entry, _State) -> ok.
apply_to_state_machine(_Entry, _State) -> ok.
create_consensus_result(_Proposal, Decision, _Data) -> 
    #consensus_result{decision = Decision, consensus_algorithm_used = raft}.
get_available_consensus_nodes() -> [node1, node2, node3, node4].
execute_byzantine_prepare_phase(_Value, _Nodes, _Security) -> {ok, []}.
execute_byzantine_commit_phase(_Value, _Quorum, _Security) -> {ok, []}.
finalize_byzantine_consensus(_Value, _Quorum) -> consensus_achieved.
verify_byzantine_safety(_Result, _Tolerance) -> safety_proof.
create_error_result(_Type, _Details) -> #consensus_result{decision = rejected}.
establish_quantum_entanglement_network(_Nodes, _Strength) -> {ok, entangled_network}.
create_consensus_superposition(_Value, _Network) -> superposition_states.
apply_quantum_interference_for_consensus(_States) -> interference_result.
quantum_measurement_consensus(_Result) -> {measurement, collapsed_state}.
verify_quantum_consensus_properties(_State, _Network) -> quantum_proof.
execute_bottom_up_consensus(_Value, _Hierarchy, _Rules) -> bottom_up_results.
detect_hierarchical_conflicts(_Results) -> {no_conflicts, []}.
apply_conflict_resolution(_Conflicts, _Strategy) -> resolved_results.
validate_hierarchical_consensus(_Results, _Hierarchy) -> validation_result.
create_hierarchical_consensus_result(_Value, _Result) -> 
    #consensus_result{decision = accepted, consensus_algorithm_used = hierarchical}.
predict_optimal_consensus_strategy(_Model, _Value, _Context) -> predicted_strategy.
execute_predicted_consensus_strategy(_Strategy, _Value, _State) -> strategy_result.
update_ml_model_from_outcome(_Model, _Strategy, _Result) -> learning_update.
apply_adaptive_optimization(_Result, _Update) -> optimized_result.
create_ml_consensus_result(_Value, _Result, _Update) -> 
    #consensus_result{decision = accepted, consensus_algorithm_used = ml_enhanced}.
get_cluster_size() -> 5.
detect_network_partition(_Nodes) -> no_partition.
perform_periodic_partition_check() -> ok.
train_consensus_strategy_model() -> strategy_model.
train_performance_optimization_model() -> performance_model.
train_fault_prediction_model() -> fault_model.
train_adaptive_timeout_model() -> timeout_model.
execute_pbft_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, pbft).
execute_tendermint_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, tendermint).
execute_avalanche_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, avalanche).
execute_stellar_consensus_protocol(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, stellar).
execute_hashgraph_consensus(_Proposal, _Options, _State) -> create_consensus_result(proposal, accepted, hashgraph).
execute_async_consensus(_Value, _Consistency, _Timeout, _State) -> 
    #consensus_result{decision = accepted, consensus_algorithm_used = async}.
execute_federated_consensus(_Value, _Clusters, _Trust, _Quorums, _State) -> 
    #consensus_result{decision = accepted, consensus_algorithm_used = federated}.
execute_probabilistic_consensus(_Value, _Confidence, _Model, _Tolerance, _State) -> 
    #consensus_result{decision = accepted, consensus_algorithm_used = probabilistic}.