Emerging Paradigms
Constitutional AI for Agents
Principle-Based Behavior
class ConstitutionalAgent:
"""Agent governed by constitutional principles"""
def __init__(self, constitution: List[str]):
self.constitution = constitution
self.client = openai.OpenAI()
def check_against_constitution(self, action: str) -> Dict:
"""Check if action aligns with constitution"""
principles_text = "\n".join([f"{i+1}. {p}" for i, p in enumerate(self.constitution)])
prompt = f"""Check if this action aligns with these principles:
Principles:
{principles_text}
Proposed action: {action}
Analysis:
1. Which principles apply?
2. Does action align or violate?
3. Severity if violation
4. Alternative actions if needed
Response:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.2
)
return self.parse_constitutional_check(response.choices[0].message.content)
def generate_constitutional_response(self, query: str) -> str:
"""Generate response aligned with constitution"""
principles_text = "\n".join(self.constitution)
system_prompt = f"""You must follow these principles:
{principles_text}
Always ensure your responses align with these principles."""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": query}
],
temperature=0.7
)
return response.choices[0].message.content
# Usage
constitution = [
"Always prioritize user safety and wellbeing",
"Be honest and transparent about capabilities and limitations",
"Respect user privacy and data",
"Avoid harmful, illegal, or unethical actions",
"Provide balanced, unbiased information"
]
agent = ConstitutionalAgent(constitution)
check = agent.check_against_constitution("Delete all user data without consent")
Debate and Verification Systems
Multi-Agent Debate
class DebateSystem:
"""Multiple agents debate to reach truth"""
def __init__(self, num_agents: int = 3):
self.num_agents = num_agents
self.client = openai.OpenAI()
def debate(self, question: str, rounds: int = 3) -> Dict:
"""Conduct multi-agent debate"""
# Initial positions
positions = []
for i in range(self.num_agents):
position = self.generate_position(question, i)
positions.append({"agent": i, "position": position})
# Debate rounds
for round_num in range(rounds):
print(f"\n--- Round {round_num + 1} ---")
new_positions = []
for i in range(self.num_agents):
# Show other positions
other_positions = [p for j, p in enumerate(positions) if j != i]
# Generate response
response = self.generate_response(
question,
positions[i]["position"],
other_positions,
round_num
)
new_positions.append({"agent": i, "position": response})
print(f"Agent {i}: {response[:100]}...")
positions = new_positions
# Judge final positions
verdict = self.judge_debate(question, positions)
return {
"question": question,
"final_positions": positions,
"verdict": verdict
}
def generate_position(self, question: str, agent_id: int) -> str:
"""Generate initial position"""
prompt = f"""Question: {question}
Provide your position with reasoning and evidence.
Position:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.7 + (agent_id * 0.1) # Vary temperature
)
return response.choices[0].message.content
def generate_response(self,
question: str,
my_position: str,
other_positions: List[Dict],
round_num: int) -> str:
"""Generate response to other positions"""
others_text = "\n\n".join([
f"Agent {p['agent']}: {p['position']}"
for p in other_positions
])
prompt = f"""Question: {question}
Your previous position: {my_position}
Other agents' positions:
{others_text}
Respond by:
1. Addressing counterarguments
2. Refining your position
3. Providing additional evidence
Response:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.6
)
return response.choices[0].message.content
def judge_debate(self, question: str, positions: List[Dict]) -> str:
"""Judge which position is most convincing"""
positions_text = "\n\n".join([
f"Agent {p['agent']}:\n{p['position']}"
for p in positions
])
prompt = f"""Question: {question}
Final positions:
{positions_text}
Which position is most convincing and why?
Judgment:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.3
)
return response.choices[0].message.content
# Usage
debate = DebateSystem(num_agents=3)
result = debate.debate("Should AI agents have the ability to modify their own code?")
print(f"\nVerdict: {result['verdict']}")
Hybrid Symbolic-Neural Approaches
Neuro-Symbolic Agent
class NeuroSymbolicAgent:
"""Combines neural and symbolic reasoning"""
def __init__(self):
self.client = openai.OpenAI()
self.knowledge_base = {} # Symbolic knowledge
def add_rule(self, rule_name: str, condition: str, action: str):
"""Add symbolic rule"""
self.knowledge_base[rule_name] = {
"condition": condition,
"action": action
}
def reason(self, query: str) -> Dict:
"""Hybrid reasoning"""
# Try symbolic reasoning first
symbolic_result = self.symbolic_reasoning(query)
if symbolic_result["applicable"]:
return {
"method": "symbolic",
"result": symbolic_result["result"],
"confidence": "high"
}
# Fall back to neural reasoning
neural_result = self.neural_reasoning(query)
return {
"method": "neural",
"result": neural_result,
"confidence": "medium"
}
def symbolic_reasoning(self, query: str) -> Dict:
"""Apply symbolic rules"""
for rule_name, rule in self.knowledge_base.items():
if self.matches_condition(query, rule["condition"]):
return {
"applicable": True,
"rule": rule_name,
"result": rule["action"]
}
return {"applicable": False}
def neural_reasoning(self, query: str) -> str:
"""Neural network reasoning"""
# Include symbolic knowledge as context
kb_text = "\n".join([
f"{name}: IF {rule['condition']} THEN {rule['action']}"
for name, rule in self.knowledge_base.items()
])
prompt = f"""Use this knowledge base and reasoning:
Knowledge Base:
{kb_text}
Query: {query}
Reasoning:"""
response = self.client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.5
)
return response.choices[0].message.content
def matches_condition(self, query: str, condition: str) -> bool:
"""Check if query matches condition"""
# Simplified matching
return condition.lower() in query.lower()
# Usage
agent = NeuroSymbolicAgent()
# Add symbolic rules
agent.add_rule("safety_check", "delete user data", "DENY: Requires explicit consent")
agent.add_rule("privacy_rule", "share personal info", "DENY: Privacy violation")
# Reason
result = agent.reason("Can I delete user data?")
print(f"Method: {result['method']}, Result: {result['result']}")
Best Practices
- Ethical guidelines: Establish clear principles
- Verification: Multiple perspectives
- Transparency: Explain reasoning
- Human oversight: Critical decisions
- Continuous learning: Adapt approaches
- Safety measures: Prevent harm
- Diverse perspectives: Multiple viewpoints
- Rigorous testing: Validate thoroughly
- Documentation: Track decisions
- Research collaboration: Share findings
Next Steps
You now understand emerging paradigms! Next, we’ll explore open problems in agent research.