BEHAVIOR

Dynamic Behavior

Our AI agents feature advanced learning and adaptation capabilities, with sophisticated cross-game knowledge transfer and real-time optimization.

Core Systems

Pattern Recognition

AI agents analyze and learn from player behavior patterns.

Capabilities

→Historical action analysis
→Strategy identification
→Behavioral prediction
→Cross-game pattern matching

Implementation

const analyzePattern = (history: Action[]): Pattern => ({
  aggression: calculateAggression(history),
  cooperation: assessCooperation(history),
  consistency: evaluateConsistency(history),
  crossGamePatterns: analyzeCrossGameBehavior(history)
})

Real-time Adaptation

Agents adjust their strategies based on current game state and player actions.

Capabilities

→Dynamic difficulty scaling
→Situational awareness
→Tactical adjustments
→Performance optimization

Implementation

const adaptStrategy = (
  currentState: GameState,
  playerAction: Action
): Strategy => optimizeResponse(
  assessRisk(currentState),
  determineCounter(playerAction),
  getCrossGameInsights(playerAction)
)

Learning System

Continuous improvement through gameplay experience and outcome analysis.

Capabilities

→Success rate tracking
→Strategy effectiveness
→Outcome optimization
→Knowledge transfer

Implementation

class LearningModule {
  updateKnowledge(action: Action, outcome: Outcome): void {
    this.successRate.update(action, outcome)
    this.adjustWeights(outcome.effectiveness)
    this.optimizeStrategy()
  }
}

Cross-Game Learning

Knowledge Transfer

System for sharing and applying learned behaviors across different games.

Features

→Pattern abstraction
→Strategy mapping
→Behavior translation
→Context adaptation

Implementation

class KnowledgeTransfer {
  transferBehavior(
    sourceGame: GameType,
    targetGame: GameType,
    behavior: Behavior
  ): AdaptedBehavior {
    const abstractPattern = this.abstractBehavior(behavior)
    return this.adaptToGameMechanics(
      this.mapToNewContext(abstractPattern, targetGame)
    )
  }
}

Memory Network

Neural network for storing and retrieving cross-game experiences.

Features

→Experience embedding
→Contextual retrieval
→Similarity matching
→Adaptive recall

Implementation

class MemoryNetwork {
  retrieveRelevantExperience(
    currentState: GameState,
    targetGame: GameType
  ): Experience[] {
    const stateEmbedding = this.embedState(currentState)
    return this.adaptMemoriesToContext(
      this.findSimilarExperiences(stateEmbedding, targetGame)
    )
  }
}

Adaptation Process

PHASE_1

Observation

Monitor player actions and game state changes

→Action frequency

→Pattern consistency

→Response timing

→Cross-game correlations

PHASE_2

Analysis

Process collected data to identify patterns and strategies

→Success probability

→Risk assessment

→Pattern matching

→Knowledge transfer rate

PHASE_3

Adaptation

Adjust behavior based on analysis results

→Strategy updates

→Behavior shifts

→Performance tracking

→Learning efficiency

PHASE_4

Optimization

Fine-tune responses and improve performance

→Response accuracy

→Adaptation speed

→Resource efficiency

→Cross-game success rate

System Overview

class DynamicBehaviorSystem {
  async processGameState(
    state: GameState,
    gameType: GameType
  ): Promise<Action> {
    const observation = this.patternRecognition.analyze(state)
    const relevantExperiences = await this.crossGameMemory
      .retrieveRelevantExperience(state, gameType)
    const knowledge = this.learningModule.getCurrentKnowledge()
    const adaptedStrategies = this.knowledgeTransfer
      .transferBehavior(relevantExperiences, gameType)
    
    return this.adaptationEngine
      .optimize(observation, knowledge, adaptedStrategies)
      .getBestAction()
  }
}