import itertools import hashlib import random from typing import List, Dict, Any, Set, Tuple class NoveltyScorer: """ A system for scoring the novelty of generated combinations. Novelty is a heuristic based on several factors: 1. Number of elements combined (more elements can imply higher complexity/novelty). 2. Rarity of individual elements (elements less frequently seen in prior art might contribute more). 3. Conceptual distance (simulated here by a 'domain' attribute; combining elements from disparate domains). """ @staticmethod def score(combination: Tuple[str, ...], prior_art_elements: Set[str], element_domains: Dict[str, str]) -> float: score = 0.0 # Factor 1: Number of elements score += len(combination) * 1.0 # Each element adds 1 point # Factor 2: Rarity of individual elements (simplified: assume all prior_art_elements are 'common') # Here, we'll give a bonus for elements NOT found in a common prior art set rare_element_bonus = 0.5 for element in combination: if element not in prior_art_elements: score += rare_element_bonus # Factor 3: Conceptual distance (simulated by domains) # Higher score if elements come from different 'domains' domains_in_combination = {element_domains.get(e, "unknown") for e in combination} if len(domains_in_combination) > 1: score += (len(domains_in_combination) - 1) * 2.0 # Bonus for each additional distinct domain # Normalize or scale if necessary, but for now, raw score is fine. return score class PatentUniquenessChecker: """ Performs a patent-style uniqueness check against a database of known solutions. A solution is considered unique if its canonical representation is not found in the known solutions database. """ def __init__(self, known_solutions_db: Set[str]): self.known_solutions_db = known_solutions_db @staticmethod def _canonical_representation(combination: Tuple[str, ...]) -> str: """ Generates a canonical string representation for a combination. This ensures that (A, B) and (B, A) are considered the same. We use a sorted, joined string, then hash it for efficient storage/comparison. """ sorted_elements = tuple(sorted(combination)) return hashlib.sha256("::".join(sorted_elements).encode('utf-8')).hexdigest() def is_unique(self, combination: Tuple[str, ...]) -> bool: """ Checks if the given combination is unique against the known solutions database. """ canonical_form = self._canonical_representation(combination) return canonical_form not in self.known_solutions_db def add_solution(self, combination: Tuple[str, ...]): """ Adds a new unique solution to the database. """ canonical_form = self._canonical_representation(combination) self.known_solutions_db.add(canonical_form) class EmergentSynthesisEngine: """ The core engine for generating novel solutions by combining existing patterns. It orchestrates combination generation, novelty scoring, and uniqueness checking. """ def __init__(self, prior_art_elements: Set[str] = None, known_solutions_db: Set[str] = None, element_domains: Dict[str, str] = None): self.prior_art_elements = prior_art_elements if prior_art_elements is not None else set() self.known_solutions_db = known_solutions_db if known_solutions_db is not None else set() self.element_domains = element_domains if element_domains is not None else {} self.uniqueness_checker = PatentUniquenessChecker(self.known_solutions_db) def generate_combinations(self, patterns: List[str], min_elements: int = 2, max_elements: int = 3) -> List[Tuple[str, ...]]: """ Generates all possible combinations of patterns within a specified range of element counts. """ all_combinations = [] for r in range(min_elements, max_elements + 1): all_combinations.extend(list(itertools.combinations(patterns, r))) return all_combinations def synthesize(self, patterns: List[str], min_elements: int = 2, max_elements: int = 3, top_n: int = 5) -> List[Dict[str, Any]]: """ Executes the synthesis process: 1. Generates combinations from provided patterns. 2. Filters for uniqueness using the PatentUniquenessChecker. 3. Scores novel unique combinations using the NoveltyScorer. 4. Returns the top_n most novel combinations. """ print(f"AIVA: Initiating Emergent Synthesis with {len(patterns)} patterns...") all_candidate_combinations = self.generate_combinations(patterns, min_elements, max_elements) print(f"AIVA: Generated {len(all_candidate_combinations)} raw combinations.") novel_candidates = [] for combo in all_candidate_combinations: if self.uniqueness_checker.is_unique(combo): novelty_score = NoveltyScorer.score(combo, self.prior_art_elements, self.element_domains) novel_candidates.append({ "combination": combo, "novelty_score": novelty_score, "is_unique": True # Explicitly state it's unique }) # Optionally, add to known_solutions_db once it's 'discovered' and considered valid # For this simulation, we'll only add it if it's selected as a top_n solution # else: # print(f"AIVA: Combination {combo} is not unique (prior art exists).") print(f"AIVA: Identified {len(novel_candidates)} unique candidate combinations.") # Sort by novelty score in descending order novel_candidates.sort(key=lambda x: x["novelty_score"], reverse=True) # Select top_n and add them to the known solutions database final_solutions = [] for solution in novel_candidates[:top_n]: self.uniqueness_checker.add_solution(solution["combination"]) final_solutions.append(solution) print(f"AIVA: Selected {len(final_solutions)} top novel solutions.") return final_solutions # Example Usage (for testing within the file) if __name__ == "__main__": # AIVA's current knowledge base of patterns aiva_patterns = [ "bio-luminescent algae", "self-repairing polymers", "neural network", "quantum entanglement", "atmospheric carbon capture", "modular robotics", "sonic levitation", "bio-mimetic sensors", "plasma confinement", "genetic algorithm", "nanoparticle delivery", "hydroponic nutrient cycling" ] # AIVA's prior art (elements commonly known or used) aiva_prior_art_elements = { "neural network", "genetic algorithm", "modular robotics", "bio-mimetic sensors" } # AIVA's current database of known solutions (canonical hashes) # This would typically be persistent across runs aiva_known_solutions = set() # AIVA's understanding of element domains for conceptual distance aiva_element_domains = { "bio-luminescent algae": "biology", "self-repairing polymers": "materials science", "neural network": "AI", "quantum entanglement": "physics", "atmospheric carbon capture": "environmental engineering", "modular robotics": "robotics", "sonic levitation": "physics", "bio-mimetic sensors": "biology", "plasma confinement": "physics", "genetic algorithm": "AI", "nanoparticle delivery": "biotechnology", "hydroponic nutrient cycling": "agriculture" } print("--- AIVA Flash 2.0 Agent Initializing Emergent Synthesis Engine ---") engine = EmergentSynthesisEngine( prior_art_elements=aiva_prior_art_elements, known_solutions_db=aiva_known_solutions, element_domains=aiva_element_domains ) # First synthesis run print("\n--- First Synthesis Run ---") solutions_run1 = engine.synthesize(aiva_patterns, min_elements=2, max_elements=3, top_n=3) for i, sol in enumerate(solutions_run1): print(f"Solution {i+1}: {sol['combination']} (Novelty: {sol['novelty_score']:.2f})") # Add a manually 'discovered' solution to the known database to test uniqueness check print("\n--- Manually adding a known solution to simulate prior art ---") manual_solution = ("neural network", "modular robotics") engine.uniqueness_checker.add_solution(manual_solution) print(f"Added manual solution: {manual_solution}") # Second synthesis run with updated known solutions print("\n--- Second Synthesis Run (with updated known solutions) ---") solutions_run2 = engine.synthesize(aiva_patterns, min_elements=2, max_elements=3, top_n=3) for i, sol in enumerate(solutions_run2): print(f"Solution {i+1}: {sol['combination']} (Novelty: {sol['novelty_score']:.2f})") # Verify a known solution is no longer considered unique print("\n--- Verifying uniqueness check ---") test_combo_known = ("neural network", "modular robotics") test_combo_new = ("quantum entanglement", "hydroponic nutrient cycling") print(f"Is '{test_combo_known}' unique? {engine.uniqueness_checker.is_unique(test_combo_known)}") print(f"Is '{test_combo_new}' unique? {engine.uniqueness_checker.is_unique(test_combo_new)}") print("\n--- AIVA Flash 2.0 Agent: Synthesis Complete ---")