[ALGO-7] Implement Alternative Algorithm Modes

[MVPandey]

🔀 ALTERNATIVE ALGORITHMS

Priority: LOW - Future Enhancement

Problem

MCTS is expensive for all scenarios. Need simpler alternatives for cost-sensitive or time-constrained situations.

Solution

Implement beam search, greedy, and hybrid algorithm modes for different use cases.

class AlgorithmMode(Enum):
    MCTS = "mcts"           # Full MCTS (highest quality, highest cost)
    BEAM_SEARCH = "beam"    # Beam search (medium quality, medium cost)
    GREEDY = "greedy"       # Greedy selection (lowest cost, fast)
    HYBRID = "hybrid"       # Adaptive selection based on context

class ConversationAnalyzer:
    async def analyze(self, conversation: Conversation, mode: AlgorithmMode = AlgorithmMode.MCTS):
        if mode == AlgorithmMode.MCTS:
            return await self.mcts_algorithm.run(conversation, config)
        elif mode == AlgorithmMode.BEAM_SEARCH:
            return await self.beam_search.run(conversation, beam_width=3)
        elif mode == AlgorithmMode.GREEDY:
            return await self.greedy_selector.run(conversation)
        elif mode == AlgorithmMode.HYBRID:
            return await self.adaptive_selector.run(conversation)

Algorithm Implementations

class BeamSearchAlgorithm:
    async def run(self, conversation: Conversation, beam_width: int = 3) -> AnalysisResult:
        """Beam search for conversation analysis"""
        current_beam = [conversation]
        
        for depth in range(self.max_depth):
            candidates = []
            
            for conversation_state in current_beam:
                # Generate response candidates
                responses = await self._generate_candidates(conversation_state, beam_width * 2)
                candidates.extend(responses)
            
            # Score and select top beam_width candidates
            scored_candidates = await self._score_candidates(candidates)
            current_beam = self._select_top_k(scored_candidates, beam_width)
            
            if self._should_stop(current_beam):
                break
        
        return self._select_best_response(current_beam)

class GreedySelector:
    async def run(self, conversation: Conversation) -> AnalysisResult:
        """Simple greedy response selection"""
        # Generate few candidates (2-3)
        candidates = await self._generate_candidates(conversation, num_candidates=3)
        
        # Score and select best
        scored_candidates = await self._score_candidates(candidates)
        return self._select_best(scored_candidates)

class AdaptiveSelector:
    async def run(self, conversation: Conversation) -> AnalysisResult:
        """Intelligently choose algorithm based on context"""
        context = await self._analyze_context(conversation)
        
        if context.complexity_score > 0.8:
            # High complexity -> use MCTS
            return await self.mcts_algorithm.run(conversation)
        elif context.time_sensitivity > 0.7:
            # Time sensitive -> use greedy
            return await self.greedy_selector.run(conversation)
        else:
            # Balanced -> use beam search
            return await self.beam_search.run(conversation, beam_width=3)

Implementation Steps

• Implement beam search algorithm
• Create greedy response selector
• Add adaptive algorithm selection
• Create algorithm performance comparison
• Add algorithm selection API parameter

Expected Benefits

• Cost-sensitive options for budget-constrained scenarios
• Fast response modes for time-critical applications
• Adaptive selection based on conversation complexity

Effort: High (1-2 weeks)