2nd/10_Wiki/Topics/Other/Problem Solving Process.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-problem-solving-process	Problem Solving Process	10_Wiki/Topics	verified	self	Problem Solving Polya Method Engineering Problem Solving	none	A	0.9	applied	cognition engineering methodology debugging		2026-05-10	pending	language framework multi structured-problem-solving

Problem Solving Process

매 한 줄

"매 a problem well-stated is half-solved.". 매 Polya 1945 How to Solve It 의 4단계 (understand → plan → carry out → look back) 가 매 modern engineering, debugging, AI agent design 의 backbone. 매 2026 LLM agent (Claude, OpenAI Operator) 의 ReAct/CoT 도 매 본질적으로 Polya 의 자동화.

매 핵심

매 Polya 4 stages

• Understand: 매 restate, 매 identify knowns/unknowns/constraints.
• Plan: 매 decompose, 매 analogous problems, 매 work backwards.
• Carry Out: 매 execute, 매 verify each step.
• Look Back: 매 check, 매 generalize, 매 alternate methods.

매 strategies

• Decomposition: 매 break into sub-problems (divide & conquer).
• Analogy: 매 find similar solved problem (case-based reasoning).
• Inversion: 매 work backwards from goal.
• Specialization: 매 try simpler instance (n=1, n=2 first).
• Generalization: 매 solve more general version sometimes easier.
• Symmetry / Invariants: 매 find quantity that doesn't change.

매 응용

1. Debugging: 매 reproduce → bisect → fix → verify → write test.
2. System design: 매 understand requirements → decompose → component design.
3. LLM agent: 매 ReAct loop = Polya in code.
4. Math/algorithms: 매 examples → conjecture → prove → optimize.

💻 패턴

Pattern 1: Debugging as Polya

def debug(bug_report):
    # 1. UNDERSTAND
    repro = build_minimal_repro(bug_report)
    # 2. PLAN
    suspected_modules = trace_stack(repro)
    plan = git_bisect_plan(repro, suspected_modules)
    # 3. CARRY OUT
    bad_commit = git_bisect(plan)
    fix = author_fix(bad_commit)
    # 4. LOOK BACK
    add_regression_test(repro)
    update_runbook(bug_report.symptom)
    return fix

Pattern 2: ReAct LLM agent (Polya 자동화)

SYSTEM = """For each task, follow:
1. THOUGHT: restate the problem and constraints (UNDERSTAND).
2. PLAN: decompose into 1-3 next actions.
3. ACTION: call exactly one tool.
4. OBSERVE: read result.
5. REFLECT: did this advance? if not, revise plan (LOOK BACK).
Repeat until done.
"""

Pattern 3: Decomposition tree

@dataclass
class Problem:
    statement: str
    children: list["Problem"] = field(default_factory=list)
    solved: bool = False
    solution: Optional[str] = None

def solve(p: Problem):
    if can_solve_directly(p):
        p.solution = direct(p); p.solved = True; return
    p.children = decompose(p)
    for c in p.children: solve(c)
    p.solution = combine([c.solution for c in p.children])
    p.solved = all(c.solved for c in p.children)

Pattern 4: Five-Whys root cause

Symptom: 매 dashboard p99 latency 5x baseline.
Why? — 매 DB queries slow.
Why? — 매 missing index.
Why? — 매 migration didn't add it.
Why? — 매 PR template doesn't require index check.
Why? — 매 no automated linter.
→ 매 Root: missing tooling, not "lazy engineer".

Pattern 5: Pre-mortem (inverted planning)

def pre_mortem(plan):
    # 매 imagine the plan failed in 6 months
    # 매 ask team: what went wrong?
    failure_modes = team_brainstorm("It's 6mo from now. Project failed. Why?")
    return prioritize_mitigations(failure_modes)

Pattern 6: Working-memory checkpoint

<!-- 매 problem_log.md while solving -->
## 매 KNOWN
- API returns 500 on POST /orders > 1000 items.
## 매 UNKNOWN
- Is it timeout, memory, or DB lock?
## 매 TRIED
- [x] Reproduce locally → reproduces at 1500.
- [x] Add timing logs → DB INSERT is slow.
- [ ] Check lock contention.
## 매 NEXT
- pg_stat_activity during repro.

Pattern 7: Solution generalization (look back)

# 매 after fixing one instance — 매 ask: where else does this pattern occur?
def generalize(fix):
    pattern = abstract(fix)  # e.g., "missing pagination in list endpoints"
    similar = scan_codebase_for(pattern)
    return apply_fix_to_all(similar)

매 결정 기준

상황	Strategy
매 stuck at "understand"	Restate problem to rubber duck / LLM
매 plan unclear	Try simpler case (n=1) first
매 carry-out fails	Bisect; isolate variable
매 done — but is it right?	Look back; alt method; edge cases
매 recurring class of bugs	Generalize the fix; tooling
매 LLM agent loop stuck	Force REFLECT step; reduce action set

기본값: 매 always do Look Back — 매 most engineers skip it; 매 90% of compounding leverage lives there.

🔗 Graph

• 부모: Cognitive Psychology
• 변형: Polya Method · Debugging · Root Cause Analysis
• Adjacent: ReAct · Chain of Thought

🤖 LLM 활용

언제: 매 system-prompt scaffolds, 매 incident runbooks, 매 onboarding docs, 매 interview prep. 언제 X: 매 trivial 1-line tasks — 매 over-formalization slows.

❌ 안티패턴

• Skip understanding: 매 jump to coding → 매 wrong problem solved.
• Skip looking back: 매 ship fix, never abstract → 매 same bug class returns.
• No working-memory log: 매 forget what you tried.
• One strategy only: 매 if decomposition fails, try inversion / analogy.
• LLM as oracle: 매 use as plan-critic, not plan-author.

🧪 검증 / 중복

• Verified (Polya 1945, Newell & Simon 1972, Yao et al. 2022 ReAct).
• 신뢰도 A (foundational).

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Polya 4단계 + ReAct + 7 패턴