/**
 * ACT-R Activation Engine
 *
 * Backend src/core/actr/engine.py 의 TypeScript 포트.
 * Power Law of Practice + Forgetting Curve 기반 활성화 계산.
 *
 * 공식:
 *   B_i = ln(sum(t_j^(-d))) + B      // Base-level (시간 감쇠)
 *   S_i = sum(W_j * S_ji)            // Spreading (컨텍스트 연관)
 *   P_i = -P * (1 - similarity)      // Partial Match (유사도 보정)
 *   A_i = B_i + S_i + P_i            // Total Activation
 */
import type { ACTRConfig, ActivationResult } from './types.js';
export declare class ACTREngine {
    private config;
    constructor(config: ACTRConfig);
    /**
     * 전체 활성화 계산
     *
     * @param accessTimes - 접근 시간 배열 (현재 시점에서의 경과 초, 양수)
     * @param associations - [{ source, strength }] 연관 소스와 강도
     * @param context - 현재 활성 컨텍스트 소스 집합
     * @param similarity - 유사도 (0.0-1.0, 1.0=완벽 일치)
     * @param memoryId - 식별용 ID
     */
    computeActivation(accessTimes: number[], associations: Array<{
        source: string;
        strength: number;
    }>, context: Set<string>, similarity: number, memoryId?: string): ActivationResult;
    /**
     * Base-level Activation: B_i = ln(sum(t_j^(-d))) + B
     * Power Law of Practice + Forgetting Curve
     */
    computeBaseLevel(accessTimes: number[]): number;
    /**
     * Spreading Activation: S_i = sum(W_j * S_ji)
     * Fan Effect: W_j = Smax / fan(j)
     */
    computeSpreading(associations: Array<{
        source: string;
        strength: number;
    }>, context: Set<string>): number;
    /**
     * Partial Matching: P_i = -P * (1 - similarity)
     */
    computePartialMatch(similarity: number): number;
    /**
     * Activation을 1-maxLevel 레벨로 변환
     *
     * 정규화: activation [-2, 5] -> [0, 1]
     * 레벨: ceil(normalized * maxLevel), clamp [1, maxLevel]
     */
    computeLevel(activation: number, maxLevel: number): number;
}
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