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"""
Knowledge Agent - Autonomous knowledge state management
"""
import json
from .base_agent import BaseAgent
from knowledge_tracker import KnowledgeTracker
from agent_knowledge.rules.knowledge_rules import KnowledgeRuleEngine
from models import db, KnowledgeState
import math
from datetime import datetime, timedelta
class KnowledgeAgent(BaseAgent):
"""
Autonomous agent responsible for:
- Tracking knowledge evolution
- Predicting future performance
- Identifying knowledge gaps
- Suggesting review topics
"""
def __init__(self):
super().__init__("KA-001", "KnowledgeAgent")
self.tracker = KnowledgeTracker()
self.rule_engine = KnowledgeRuleEngine()
self.predictions_made = 0
self.updates_performed = 0
def perceive(self, student_state):
"""
Perceive student's learning state
"""
self.update_state("perceiving")
self.user_id = getattr(student_state, 'user_id', None)
self.log(f"Perceiving knowledge state for user {self.user_id}")
self.topic_id = getattr(student_state, 'topic_id', None)
# Get all knowledge states
if self.user_id:
self.knowledge_states = KnowledgeState.query.filter_by(
user_id=self.user_id
).all()
else:
self.knowledge_states = []
self.log(f"Found {len(self.knowledge_states)} knowledge states")
return self
def decide(self):
"""
Autonomous decisions:
- Which topics need review?
- What's the optimal learning path?
- Are there knowledge gaps?
"""
self.update_state("deciding")
# Decision 1: Apply forgetting curves
topics_needing_review = []
for state in self.knowledge_states:
days_since_practice = (datetime.utcnow() - state.last_practiced).days
if days_since_practice > 7 and state.knowledge_level > 0.3:
topics_needing_review.append({
'topic_id': state.topic_id,
'days_since': days_since_practice,
'current_level': state.knowledge_level,
'priority': days_since_practice * (1 - state.knowledge_level)
})
# Sort by priority
topics_needing_review.sort(key=lambda x: x['priority'], reverse=True)
self.review_recommendations = topics_needing_review[:3]
# Decision 2: Identify knowledge gaps
self.knowledge_gaps = [
state for state in self.knowledge_states
if 0 < state.knowledge_level < 0.4
]
# Decision 3: Predict next knowledge level
if self.topic_id:
self.predicted_next_level = self._predict_knowledge_growth(self.topic_id)
else:
self.predicted_next_level = None
self.log(f"Decisions: {len(self.review_recommendations)} topics need review, "
f"{len(self.knowledge_gaps)} knowledge gaps found")
return self
def _predict_knowledge_growth(self, topic_id):
"""Predict knowledge growth based on learning patterns"""
state = KnowledgeState.query.filter_by(
user_id=self.user_id,
topic_id=topic_id
).first()
if not state:
return 0.15 # Expected growth for new topic
# Predict based on practice frequency and current level
if state.practice_count > 5:
# Experienced learner - slower growth
growth = 0.1 * (1 - state.knowledge_level)
else:
# New learner - faster initial growth
growth = 0.15 * (1 - state.knowledge_level)
predicted = min(1.0, state.knowledge_level + growth)
self.predictions_made += 1
self.log(f"Predicted knowledge growth: {state.knowledge_level:.2f} → {predicted:.2f}")
return predicted
def act(self, user_id=None, action_type="monitoring", **kwargs):
"""
Execute: Update knowledge states and provide recommendations
"""
self.update_state("acting")
if user_id:
self.user_id = user_id
# Dispatch based on action type
if action_type == 'update_state':
return self.update_knowledge_from_quiz(
kwargs.get('topic_id'),
kwargs.get('is_correct'),
kwargs.get('difficulty')
)
# Default / Monitoring / get_weak_areas logic
self.log(f"performing {action_type}...")
# Apply forgetting curves to all states
updated_states = []
for state in self.knowledge_states:
days_since = (datetime.utcnow() - state.last_practiced).days
if days_since > 0:
# Apply exponential forgetting
forgetting_factor = math.exp(-0.05 * days_since)
old_level = state.knowledge_level
state.knowledge_level *= forgetting_factor
if abs(old_level - state.knowledge_level) > 0.01:
updated_states.append({
'topic_id': state.topic_id,
'old_level': old_level,
'new_level': state.knowledge_level,
'decay': old_level - state.knowledge_level
})
self.updates_performed += 1
# Commit updates
if updated_states:
db.session.commit()
self.log(f"Updated {len(updated_states)} knowledge states")
self.update_state("completed")
return {
"review_recommendations": getattr(self, 'review_recommendations', []),
"knowledge_gaps": [
{
'topic_id': gap.topic_id,
'level': gap.knowledge_level,
'confidence': gap.confidence
}
for gap in getattr(self, 'knowledge_gaps', [])
],
"predicted_growth": getattr(self, 'predicted_next_level', None),
"updated_states": updated_states,
"agent": self.name,
"timestamp": datetime.utcnow().isoformat()
}
def update_knowledge_from_quiz(self, topic_id, is_correct, difficulty):
"""
Autonomous knowledge update based on quiz performance
"""
self.update_state("updating")
self.log(f"Updating knowledge for topic {topic_id}, correct={is_correct}")
# Use existing tracker but with agent logging
state = self.tracker.update_knowledge(
self.user_id,
topic_id,
is_correct,
difficulty
)
self.updates_performed += 1
self.log(f"Knowledge updated: {state.knowledge_level:.2f}, "
f"confidence: {state.confidence:.2f}")
return {
"new_level": state.knowledge_level,
"confidence": state.confidence,
"practice_count": state.practice_count,
"agent": self.name
}
def get_statistics(self):
"""Return agent statistics"""
return {
"agent": self.name,
"predictions_made": self.predictions_made,
"updates_performed": self.updates_performed,
"state": self.state,
"memory_size": len(self.memory)
} |