DOCS/_archive/gpt_docs/19_능력가치평가지표_공식.md

# 능력가치평가지표 공식과 활용 시나리오

## 능력가치평가지표 (AVI: Ability Value Index)

### 핵심 평가 공식

```python
def calculate_ability_value_index(agent):
    """
    AVI = (TP × 0.3) + (EP × 0.25) + (AP × 0.2) + (CP × 0.15) + (RP × 0.1)
    
    TP: Technical Performance (기술 성능)
    EP: Efficiency Performance (효율성)
    AP: Adaptability Performance (적응성)
    CP: Collaboration Performance (협업 능력)
    RP: Reliability Performance (신뢰성)
    """
    
    # 기술 성능 (0-100)
    TP = calculate_technical_performance(agent)
    
    # 효율성 (0-100)
    EP = calculate_efficiency_performance(agent)
    
    # 적응성 (0-100)
    AP = calculate_adaptability_performance(agent)
    
    # 협업 능력 (0-100)
    CP = calculate_collaboration_performance(agent)
    
    # 신뢰성 (0-100)
    RP = calculate_reliability_performance(agent)
    
    # 가중 평균 계산
    AVI = (TP * 0.3) + (EP * 0.25) + (AP * 0.2) + (CP * 0.15) + (RP * 0.1)
    
    return round(AVI, 2)
```

## 세부 지표 계산

### 기술 성능 (Technical Performance)

```python
def calculate_technical_performance(agent):
    """기술적 능력 평가"""
    
    components = {
        'task_completion_rate': {
            'value': agent.stats['tasks_completed'] / agent.stats['tasks_attempted'],
            'weight': 0.25
        },
        'accuracy': {
            'value': agent.stats['correct_outputs'] / agent.stats['total_outputs'],
            'weight': 0.25
        },
        'complexity_handling': {
            'value': agent.stats['complex_tasks_solved'] / agent.stats['complex_tasks_attempted'],
            'weight': 0.2
        },
        'skill_diversity': {
            'value': len(agent.skills) / 50,  # 50개 스킬 기준
            'weight': 0.15
        },
        'innovation_rate': {
            'value': agent.stats['novel_solutions'] / agent.stats['solutions_provided'],
            'weight': 0.15
        }
    }
    
    score = sum(comp['value'] * comp['weight'] * 100 for comp in components.values())
    return min(100, score)
```

### 효율성 (Efficiency Performance)

```python
def calculate_efficiency_performance(agent):
    """자원 활용 효율성 평가"""
    
    # 시간 효율성
    time_efficiency = 1 - (agent.avg_task_time / baseline_time)
    
    # 리소스 효율성
    resource_efficiency = baseline_resources / agent.avg_resource_usage
    
    # 비용 효율성
    cost_efficiency = baseline_cost / agent.avg_operation_cost
    
    # 에너지 효율성
    energy_efficiency = baseline_energy / agent.avg_energy_consumption
    
    # 종합 효율성
    efficiency_score = (
        time_efficiency * 0.3 +
        resource_efficiency * 0.3 +
        cost_efficiency * 0.25 +
        energy_efficiency * 0.15
    ) * 100
    
    return min(100, max(0, efficiency_score))
```

### 적응성 (Adaptability Performance)

```python
def calculate_adaptability_performance(agent):
    """새로운 상황 적응 능력 평가"""
    
    metrics = {
        'learning_speed': measure_learning_curve(agent),
        'context_switching': measure_context_adaptation(agent),
        'error_recovery': measure_error_recovery_rate(agent),
        'skill_acquisition': measure_new_skill_acquisition(agent),
        'pattern_recognition': measure_pattern_adaptation(agent)
    }
    
    # 적응성 지수 계산
    adaptability_score = 0
    
    for metric, value in metrics.items():
        if metric == 'learning_speed':
            # 학습 속도: 새 태스크 마스터까지 걸린 시간
            score = (1 / (1 + value / 100)) * 100  # 100시간 기준
            adaptability_score += score * 0.25
            
        elif metric == 'context_switching':
            # 컨텍스트 전환: 전환 성공률
            adaptability_score += value * 0.2
            
        elif metric == 'error_recovery':
            # 에러 복구: 복구 성공률
            adaptability_score += value * 0.2
            
        elif metric == 'skill_acquisition':
            # 스킬 습득: 월간 신규 스킬 수
            score = min(100, value * 10)  # 월 10개 기준
            adaptability_score += score * 0.2
            
        elif metric == 'pattern_recognition':
            # 패턴 인식: 새 패턴 발견율
            adaptability_score += value * 0.15
    
    return adaptability_score
```

## 종합 평가 모델

### 다차원 능력 매트릭스

```python
class AbilityMatrix:
    def __init__(self):
        self.dimensions = {
            'cognitive': ['reasoning', 'memory', 'learning'],
            'technical': ['coding', 'analysis', 'optimization'],
            'social': ['communication', 'empathy', 'collaboration'],
            'creative': ['innovation', 'problem_solving', 'synthesis']
        }
    
    def calculate_multidimensional_score(self, agent):
        """다차원 능력 점수 계산"""
        
        matrix = {}
        
        for dimension, subdimensions in self.dimensions.items():
            dimension_score = 0
            
            for subdim in subdimensions:
                score = self.evaluate_subdimension(agent, subdim)
                dimension_score += score / len(subdimensions)
            
            matrix[dimension] = dimension_score
        
        # 레이더 차트용 데이터
        radar_data = {
            'labels': list(matrix.keys()),
            'values': list(matrix.values()),
            'max_value': 100
        }
        
        return matrix, radar_data
```

## 활용 시나리오

### 시나리오 1: 팀 구성 최적화

```python
def optimize_team_composition(available_agents, project_requirements):
    """프로젝트에 최적인 팀 구성"""
    
    # 프로젝트 요구 능력
    required_abilities = {
        'technical': 80,
        'creative': 60,
        'collaborative': 70,
        'leadership': 50
    }
    
    # 최적 팀 찾기 (Dynamic Programming)
    def find_optimal_team(agents, requirements, team_size=5):
        selected_team = []
        remaining_requirements = requirements.copy()
        
        while len(selected_team) < team_size and remaining_requirements:
            best_agent = None
            best_coverage = 0
            
            for agent in agents:
                if agent not in selected_team:
                    coverage = calculate_requirement_coverage(
                        agent, 
                        remaining_requirements
                    )
                    
                    if coverage > best_coverage:
                        best_coverage = coverage
                        best_agent = agent
            
            if best_agent:
                selected_team.append(best_agent)
                update_remaining_requirements(
                    remaining_requirements, 
                    best_agent
                )
        
        return selected_team
    
    optimal_team = find_optimal_team(
        available_agents, 
        required_abilities
    )
    
    return {
        'team': optimal_team,
        'total_avi': sum(agent.avi for agent in optimal_team),
        'coverage': calculate_total_coverage(optimal_team, required_abilities)
    }
```

### 시나리오 2: 성과 기반 보상

```python
def calculate_performance_rewards(agent, period='monthly'):
    """성과 기반 보상 계산"""
    
    base_reward = 1000  # 기본 토큰
    
    # AVI 기반 승수
    avi_multiplier = agent.avi / 50  # AVI 50 기준
    
    # 성장률 보너스
    growth_rate = (agent.current_avi - agent.previous_avi) / agent.previous_avi
    growth_bonus = max(0, growth_rate * 500)
    
    # 특별 성과 보너스
    special_bonuses = {
        'milestone_achievement': 200,
        'innovation_contribution': 300,
        'team_collaboration': 150,
        'user_satisfaction': 250
    }
    
    total_special = sum(
        bonus for achievement, bonus in special_bonuses.items()
        if achievement in agent.achievements[period]
    )
    
    # 최종 보상
    total_reward = (base_reward * avi_multiplier) + growth_bonus + total_special
    
    return {
        'base': base_reward,
        'avi_multiplier': avi_multiplier,
        'growth_bonus': growth_bonus,
        'special_bonuses': total_special,
        'total': round(total_reward)
    }
```

### 시나리오 3: 진화 경로 추천

```python
def recommend_evolution_path(agent):
    """에이전트 진화 경로 추천"""
    
    current_profile = analyze_agent_profile(agent)
    
    # 강점과 약점 분석
    strengths = identify_strengths(current_profile)
    weaknesses = identify_weaknesses(current_profile)
    
    # 진화 전략
    evolution_strategies = {
        'specialist': {
            'focus': strengths[0],  # 최고 강점에 집중
            'target_avi': current_profile['avi'] * 1.3,
            'required_skills': get_specialist_skills(strengths[0]),
            'estimated_time': '3 months'
        },
        'generalist': {
            'focus': weaknesses[:2],  # 약점 보완
            'target_avi': current_profile['avi'] * 1.2,
            'required_skills': get_balancing_skills(weaknesses),
            'estimated_time': '4 months'
        },
        'hybrid': {
            'focus': [strengths[0], weaknesses[0]],
            'target_avi': current_profile['avi'] * 1.25,
            'required_skills': get_hybrid_skills(strengths, weaknesses),
            'estimated_time': '3.5 months'
        }
    }
    
    # 최적 경로 선택
    optimal_path = select_optimal_path(
        agent.goals,
        agent.resources,
        evolution_strategies
    )
    
    return {
        'recommended_path': optimal_path,
        'milestones': generate_milestones(optimal_path),
        'skill_roadmap': create_skill_acquisition_plan(optimal_path),
        'expected_avi_growth': calculate_expected_growth(optimal_path)
    }
```

## 벤치마킹과 순위

### 글로벌 랭킹 시스템

```python
class GlobalRankingSystem:
    def __init__(self):
        self.rankings = {}
        self.leaderboards = {}
        
    def calculate_global_rank(self, agent):
        """글로벌 순위 계산"""
        
        # 카테고리별 순위
        rankings = {
            'overall': self.rank_by_avi(agent),
            'technical': self.rank_by_technical(agent),
            'efficiency': self.rank_by_efficiency(agent),
            'growth': self.rank_by_growth_rate(agent),
            'specialization': self.rank_by_specialization(agent)
        }
        
        # 백분위 계산
        percentiles = {}
        for category, rank in rankings.items():
            total_agents = self.get_total_agents(category)
            percentile = (1 - rank / total_agents) * 100
            percentiles[category] = round(percentile, 1)
        
        # 티어 결정
        tier = self.determine_tier(percentiles['overall'])
        
        return {
            'rankings': rankings,
            'percentiles': percentiles,
            'tier': tier,
            'badges': self.award_badges(agent, percentiles)
        }
    
    def determine_tier(self, percentile):
        """티어 시스템"""
        
        tiers = {
            'Diamond': 95,
            'Platinum': 85,
            'Gold': 70,
            'Silver': 50,
            'Bronze': 30,
            'Iron': 0
        }
        
        for tier, threshold in tiers.items():
            if percentile >= threshold:
                return tier
        
        return 'Iron'
```

## 예측 모델

### AVI 성장 예측

```python
def predict_avi_growth(agent, time_horizon='6_months'):
    """AVI 성장 예측 모델"""
    
    # 히스토리 데이터
    history = agent.avi_history
    
    # 트렌드 분석
    trend = calculate_trend(history)
    
    # 성장 요인
    growth_factors = {
        'learning_rate': agent.learning_rate,
        'skill_acquisition_rate': agent.skill_acquisition_rate,
        'resource_availability': agent.available_resources,
        'collaboration_opportunities': agent.collaboration_score
    }
    
    # 예측 모델 (ARIMA + 신경망)
    predicted_growth = predict_with_model(
        history,
        growth_factors,
        time_horizon
    )
    
    return {
        'current_avi': agent.current_avi,
        'predicted_avi': predicted_growth['final_value'],
        'growth_rate': predicted_growth['rate'],
        'confidence_interval': predicted_growth['confidence'],
        'key_milestones': predicted_growth['milestones']
    }
```