高级认知程序

"简单的事情应该简单,复杂的事情应该可能。" — Alan Kay

概述

高级认知程序建立在基本编程模式之上,创建更复杂的推理框架。这些程序融合了高阶函数、动态组合、元编程和自我改进循环,以处理需要适应性和细微差别的复杂推理任务。

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│  ADVANCED PROGRAM ARCHITECTURE                               │
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│  │             │     │             │     │             │     │
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高级编程模式

1. 高阶函数

高阶函数将其他函数作为输入或返回它们作为输出,实现强大的抽象和可组合性。

function applyReasoningStrategy(problem, strategy, options = {}) {
  // Higher-order function that applies different reasoning strategies

  // Strategy functions that can be passed in
  const strategies = {
    decomposition: function(p) {
      return `
        Task: Solve this problem by breaking it into smaller sub-problems.

        Problem: ${p}

        Process:
        1. Identify the main components of the problem
        2. Break the problem into distinct sub-problems
        3. Solve each sub-problem individually
        4. Integrate the solutions to solve the complete problem

        Start by clearly stating each sub-problem before solving it.
      `;
    },

    analogy: function(p) {
      return `
        Task: Solve this problem by finding an analogous simpler problem.

        Problem: ${p}

        Process:
        1. Identify the underlying structure of the problem
        2. Recall a similar problem with a known solution
        3. Map the elements from the known problem to this problem
        4. Adapt the known solution to fit this problem

        Start by explicitly stating the analogy you're using.
      `;
    },

    firstPrinciples: function(p) {
      return `
        Task: Solve this problem using first principles reasoning.

        Problem: ${p}

        Process:
        1. Identify the fundamental truths or principles relevant to this problem
        2. Break down the problem to these essential elements
        3. Build a solution from the ground up
        4. Verify the solution using these principles

        Start by clearly stating the fundamental principles you're using.
      `;
    }
  };

  // If strategy is a string, use one of the predefined strategies
  if (typeof strategy === 'string') {
    if (!strategies[strategy]) {
      throw new Error(`Unknown strategy: ${strategy}`);
    }
    return strategies[strategy](problem);
  }

  // If strategy is a function, apply it directly
  if (typeof strategy === 'function') {
    return strategy(problem, options);
  }

  throw new Error('Strategy must be a string or function');
}

// Custom strategy function
function socraticMethod(problem, options = {}) {
  const questions = options.questions || [
    "What are the key concepts involved?",
    "What assumptions are we making?",
    "What would happen if those assumptions were different?",
    "Can we break this down into simpler questions?",
    "What analogous problems have we solved before?"
  ];

  return `
    Task: Explore this problem using the Socratic method.

    Problem: ${problem}

    Process:
    Ask and answer a series of probing questions:
    ${questions.map((q, i) => `${i+1}. ${q}`).join('\n')}

    For each question, provide a thoughtful answer before moving to the next question.
    After exploring all questions, synthesize your insights to solve the original problem.
  `;
}

// Usage examples
const decompositionPrompt = applyReasoningStrategy(
  "How might climate change affect global agriculture by 2050?",
  "decomposition"
);

const socraticPrompt = applyReasoningStrategy(
  "Is artificial intelligence more likely to help or harm humanity?",
  socraticMethod,
  { questions: [
    "What do we mean by 'help' and 'harm'?",
    "What assumptions are we making about AI development?",
    "What historical analogies might be relevant?",
    "What are the key risks and benefits to consider?",
    "How might different stakeholders be affected differently?"
  ]}
);

关键概念:

• 策略函数: 可以作为参数传递的推理方法
• 函数类型检查: 处理字符串和函数策略
• 自定义策略: 创建专门的推理方法

2. 装饰器模式

装饰器在不改变核心实现的情况下修改函数的行为,实现分层增强。

function withExampleGeneration(reasoningFunction) {
  // Decorator that adds example generation to any reasoning function
  return function(problem, options = {}) {
    const basePrompt = reasoningFunction(problem, options);

    // Add example generation
    return `
      ${basePrompt}

      After you've developed your solution, generate 2-3 specific examples that test your solution.
      For each example:
      1. Create a concrete instance of the problem
      2. Apply your solution approach step by step
      3. Verify the result is correct

      These examples will help validate your solution and demonstrate its application.
    `;
  };
}

function withAlternativePerspectives(reasoningFunction) {
  // Decorator that adds consideration of alternative perspectives
  return function(problem, options = {}) {
    const basePrompt = reasoningFunction(problem, options);

    // Add perspective consideration
    return `
      ${basePrompt}

      After developing your initial solution, consider at least two alternative perspectives or approaches:

      Alternative Perspective 1:
      - How would someone with a different background approach this?
      - What different assumptions might they make?
      - What insights does this perspective offer?

      Alternative Perspective 2:
      - How would a different discipline or field approach this?
      - What frameworks or methods would they apply?
      - What insights does this perspective offer?

      After exploring these alternatives, refine your original solution by incorporating valuable insights.
    `;
  };
}

// Usage examples
const standardSolver = step_by_step_reasoning;
const solverWithExamples = withExampleGeneration(step_by_step_reasoning);
const comprehensiveSolver = withAlternativePerspectives(withExampleGeneration(step_by_step_reasoning));

const prompt1 = standardSolver("Solve for x: 3x + 7 = 22");
const prompt2 = solverWithExamples("Solve for x: 3x + 7 = 22");
const prompt3 = comprehensiveSolver("How might rising interest rates affect housing markets?");

关键概念:

• 函数包装: 在不修改原函数的情况下添加功能
• 可组合装饰器: 多个装饰器可以链式组合
• 增量增强: 逐层添加复杂性

3. 自我改进程序

这些程序融合了反馈循环,使它们能够完善自己的输出。

function selfImprovingReasoner(problem, iterations = 2, options = {}) {
  // Base prompt for initial solution
  const initialPrompt = `
    Task: Solve the following problem.

    Problem: ${problem}

    Instructions:
    1. Carefully read and understand the problem
    2. Plan your approach to solving it
    3. Execute your plan step by step
    4. Verify your solution

    Provide your complete solution below.
  `;

  // Improvement prompt template
  const improvementTemplate = (solution, iteration) => `
    Task: Improve the following solution to the problem.

    Problem: ${problem}

    Current Solution (Iteration ${iteration}):
    ${solution}

    Instructions for Improvement:
    1. Critically evaluate the current solution
    2. Identify specific weaknesses, gaps, or errors
    3. Consider how to address each issue
    4. Provide an improved solution that fixes these issues

    Focus on these aspects:
    ${iteration === 1 ?
      "- Correctness: Is the solution mathematically/logically sound?\n- Completeness: Does it address all aspects of the problem?" :
      "- Clarity: Is the explanation clear and easy to follow?\n- Efficiency: Is there a more elegant or efficient approach?"}

    Provide your improved solution below.
  `;

  // Construct the complete self-improving prompt
  let fullPrompt = initialPrompt;

  for (let i = 1; i <= iterations; i++) {
    fullPrompt += `

    --- AFTER COMPLETING YOUR SOLUTION ABOVE ---

    ${improvementTemplate("[Your solution from above]", i)}
    `;
  }

  return fullPrompt;
}

// Usage
const basicPrompt = selfImprovingReasoner(
  "Design a system to reduce traffic congestion in urban areas",
  2
);

// More complex example with customization
function customSelfImprovingReasoner(problem, evaluationCriteria, iterations = 2) {
  // Initial solution prompt
  const initialPrompt = step_by_step_reasoning(problem);

  // Generate improvement phases
  let improvementPhases = "";

  for (let i = 1; i <= iterations; i++) {
    const criteriaForThisIteration = evaluationCriteria[Math.min(i-1, evaluationCriteria.length-1)];

    improvementPhases += `

    --- IMPROVEMENT PHASE ${i} ---

    Review your solution above according to these criteria:
    ${criteriaForThisIteration.map(c => `- ${c}`).join('\n')}

    For each criterion:
    1. Evaluate how well your current solution meets this criterion
    2. Identify specific ways to improve
    3. Revise your solution accordingly

    Provide your improved solution below.
    `;
  }

  return initialPrompt + improvementPhases;
}

// Example usage with custom criteria
const evaluationCriteria = [
  ["Logical soundness", "Comprehensiveness", "Evidence-based reasoning"],
  ["Clarity of explanation", "Practical feasibility", "Consideration of trade-offs"],
  ["Originality", "Ethical considerations", "Long-term implications"]
];

const customImprovedPrompt = customSelfImprovingReasoner(
  "How could genetic engineering technology be regulated to maximize benefits while minimizing risks?",
  evaluationCriteria,
  3
);

关键概念:

• 迭代改进: 多轮解决方案完善
• 评估标准: 每次迭代关注不同方面
• 自我批评: 程序评估自己的输出

4. 元编程

元编程涉及生成或修改其他程序的程序,实现动态定制。

function generateSpecializedReasoner(domain, complexity = "intermediate") {
  // This function generates a domain-specific reasoning program

  // Domain-specific knowledge and approaches
  const domainKnowledge = {
    mathematics: {
      concepts: ["equations", "functions", "geometry", "calculus", "probability"],
      approaches: ["algebraic manipulation", "geometric visualization", "numerical approximation"],
      common_mistakes: ["sign errors", "incorrect application of formulas", "calculation errors"],
      verification: ["check with examples", "verify boundary conditions", "dimensional analysis"]
    },

    ethics: {
      concepts: ["utilitarianism", "deontology", "virtue ethics", "justice", "rights"],
      approaches: ["consequentialist analysis", "principle-based reasoning", "stakeholder analysis"],
      common_mistakes: ["false dichotomies", "appeal to nature", "slippery slope arguments"],
      verification: ["consider counter-examples", "test with edge cases", "examine assumptions"]
    },

    business: {
      concepts: ["market analysis", "competitive advantage", "financial metrics", "strategy", "operations"],
      approaches: ["cost-benefit analysis", "SWOT analysis", "stakeholder mapping", "scenario planning"],
      common_mistakes: ["sunk cost fallacy", "confirmation bias", "short-term thinking"],
      verification: ["financial validation", "market testing", "sensitivity analysis"]
    }
  };

  // Complexity levels
  const complexityLevels = {
    basic: {
      steps: 3,
      depth: "Focus on fundamental concepts and straightforward applications.",
      guidance: "Provide clear, step-by-step instructions with explanations of each step."
    },

    intermediate: {
      steps: 5,
      depth: "Incorporate domain-specific techniques and address common complications.",
      guidance: "Balance guidance with opportunities for independent reasoning."
    },

    advanced: {
      steps: 7,
      depth: "Address nuanced considerations, edge cases, and theoretical implications.",
      guidance: "Provide high-level guidance while encouraging sophisticated analysis."
    }
  };

  // Check if domain is supported
  if (!domainKnowledge[domain]) {
    throw new Error(`Domain not supported: ${domain}. Supported domains: ${Object.keys(domainKnowledge).join(", ")}`);
  }

  // Check if complexity is supported
  if (!complexityLevels[complexity]) {
    throw new Error(`Complexity level not supported: ${complexity}. Supported levels: ${Object.keys(complexityLevels).join(", ")}`);
  }

  const domainInfo = domainKnowledge[domain];
  const complexityInfo = complexityLevels[complexity];

  // Generate the domain-specific reasoning function
  return function(problem, options = {}) {
    // Construct domain-specific steps
    let steps = [];

    // Common first step for all domains
    steps.push(`Understand the ${domain} problem: Identify key elements and goals.`);

    // Domain-specific steps
    if (domain === "mathematics") {
      steps.push("Identify relevant mathematical concepts and formulas.");
      steps.push("Set up the mathematical representation of the problem.");
      if (complexity !== "basic") {
        steps.push("Consider different solution approaches and select the most appropriate one.");
      }
      steps.push("Execute the solution step-by-step, showing all work.");
      if (complexity === "advanced") {
        steps.push("Consider edge cases and special conditions.");
        steps.push("Explore alternative solutions or optimizations.");
      }
    }
    else if (domain === "ethics") {
      steps.push("Identify the ethical dimensions and stakeholders involved.");
      steps.push("Analyze the problem from multiple ethical frameworks.");
      if (complexity !== "basic") {
        steps.push("Consider conflicting values and principles at play.");
      }
      steps.push("Develop reasoned ethical judgments or recommendations.");
      if (complexity === "advanced") {
        steps.push("Address potential objections and counterarguments.");
        steps.push("Explore broader implications and precedents.");
      }
    }
    else if (domain === "business") {
      steps.push("Analyze the business context and relevant market factors.");
      steps.push("Identify key business objectives and constraints.");
      if (complexity !== "basic") {
        steps.push("Evaluate multiple strategic options or approaches.");
      }
      steps.push("Develop recommendations with supporting rationale.");
      if (complexity === "advanced") {
        steps.push("Consider implementation challenges and risk mitigation.");
        steps.push("Evaluate long-term implications and sustainability.");
      }
    }

    // Common final step for all domains
    steps.push(`Verify your solution: Check for errors and ensure it addresses the original ${domain} problem.`);

    // Construct the domain-specific prompt
    return `
      Task: Solve the following ${domain} problem at a ${complexity} level.

      Problem: ${problem}

      Instructions:
      Approach this ${domain} problem using the following steps:
      ${steps.map((step, i) => `${i+1}. ${step}`).join('\n')}

      ${complexityInfo.guidance}

      Domain-Specific Guidance:
      - Relevant concepts to consider: ${domainInfo.concepts.join(', ')}
      - Useful approaches: ${domainInfo.approaches.join(', ')}
      - Common mistakes to avoid: ${domainInfo.common_mistakes.join(', ')}
      - Verification methods: ${domainInfo.verification.join(', ')}

      ${complexityInfo.depth}

      Conclude with a clear, well-justified solution to the original problem.
    `;
  };
}

// Usage examples
const mathReasoner = generateSpecializedReasoner("mathematics", "intermediate");
const ethicsReasoner = generateSpecializedReasoner("ethics", "advanced");
const businessReasoner = generateSpecializedReasoner("business", "basic");

const mathPrompt = mathReasoner("Solve for x in the equation 3x² + 7x - 22 = 0");
const ethicsPrompt = ethicsReasoner("Is it ethical for companies to collect and sell user data?");
const businessPrompt = businessReasoner("How should a retail store respond to increasing online competition?");

关键概念:

• 程序生成: 函数创建其他函数
• 领域知识: 嵌入特定领域的专业知识
• 动态定制: 基于参数调整程序行为

5. 动态编程执行

这种模式涉及动态生成和执行代码,实现超越静态提示的计算推理。

function dynamicComputationalReasoning(problem, computationalApproach = "numerical") {
  // Approaches to computational reasoning
  const approaches = {
    numerical: {
      description: "Using numerical computations to solve problems with concrete values",
      codeTemplate: `
        function solve(input) {
          // Convert the problem into numerical calculations
          // Parse any relevant numbers from the input
          const parsedValues = extractNumbers(input);

          // Set up computations
          // [Code to solve the problem numerically]

          // Return the result
          return result;
        }

        function extractNumbers(text) {
          // Extract numerical values from text
          const numbers = text.match(/\\d+(\\.\\d+)?/g) || [];
          return numbers.map(n => parseFloat(n));
        }
      `
    },

    symbolic: {
      description: "Using symbolic mathematics to solve problems with variables and equations",
      codeTemplate: `
        function solve(input) {
          // Set up symbolic variables and equations
          // [Code to parse and represent algebraic expressions]

          // Solve the equations symbolically
          // [Code to manipulate and solve equations]

          // Return the symbolic solution
          return solution;
        }
      `
    },

    probabilistic: {
      description: "Using probability and statistics to reason about uncertain outcomes",
      codeTemplate: `
        function solve(input) {
          // Set up probability distributions and parameters
          // [Code to define probability models]

          // Compute probabilities or statistical measures
          // [Code to calculate probabilistic outcomes]

          // Return the probabilistic analysis
          return analysis;
        }
      `
    },

    algorithmic: {
      description: "Using algorithms to solve computational problems step by step",
      codeTemplate: `
        function solve(input) {
          // Define the algorithm steps
          // [Code to implement the algorithm]

          // Execute the algorithm
          // [Code to run the algorithm on the input]

          // Return the result
          return result;
        }
      `
    }
  };

  // Check if approach is supported
  if (!approaches[computationalApproach]) {
    throw new Error(`Approach not supported: ${computationalApproach}. Supported approaches: ${Object.keys(approaches).join(", ")}`);
  }

  const approach = approaches[computationalApproach];

  // Construct the computational reasoning prompt
  return `
    Task: Solve the following problem using ${computationalApproach} computational reasoning.

    Problem: ${problem}

    Instructions:
    Approach this problem computationally using ${approach.description}.

    1. First, translate the problem into a computational representation.
    2. Then, develop code to solve the problem.
    3. Trace through the execution of your code step by step.
    4. Interpret the computational results in the context of the original problem.

    You may use the following code template as a starting point:

    \`\`\`javascript
    ${approach.codeTemplate}
    \`\`\`

    Modify this template as needed to solve the specific problem.

    After writing your code, trace through its execution with the given input, showing intermediate values and results.

    Finally, interpret the computational results in plain language to directly answer the original problem.
  `;
}

// Usage examples
const numericalPrompt = dynamicComputationalReasoning(
  "If a car travels at 60 mph for 2.5 hours, how far does it go?",
  "numerical"
);

const symbolicPrompt = dynamicComputationalReasoning(
  "Find the general solution to the differential equation dy/dx = 2x + y",
  "symbolic"
);

const probabilisticPrompt = dynamicComputationalReasoning(
  "If a fair coin is flipped 10 times, what is the probability of getting exactly 7 heads?",
  "probabilistic"
);

const algorithmicPrompt = dynamicComputationalReasoning(
  "Find the shortest path between nodes A and F in the given graph",
  "algorithmic"
);

关键概念:

• 计算方法: 数值、符号、概率和算法方法
• 代码模板: 用于不同类型推理的起点
• 执行跟踪: 逐步展示计算过程

6. 动态协议生成

这种模式根据任务需求动态生成结构化交互协议。

function generateTaskProtocol(task, participantRoles, options = {}) {
  // Default options
  const defaults = {
    interactionSteps: 4,
    outputFormat: "structured",  // Can be "structured", "narrative", "hybrid"
    qualityChecks: true,
    adaptationRules: true
  };

  // Merge defaults with provided options
  const settings = {...defaults, ...options};

  // Ensure participantRoles is an array
  const roles = Array.isArray(participantRoles) ? participantRoles : [participantRoles];

  // Generic interaction protocol steps
  const protocolSteps = [
    {
      name: "Task Analysis",
      description: "Analyze and break down the task into components",
      roleActions: roles.reduce((actions, role) => {
        actions[role] = getAnalysisAction(role, task);
        return actions;
      }, {})
    },
    {
      name: "Information Gathering",
      description: "Collect relevant information and resources",
      roleActions: roles.reduce((actions, role) => {
        actions[role] = getInformationAction(role, task);
        return actions;
      }, {})
    },
    {
      name: "Solution Development",
      description: "Develop potential solutions or approaches",
      roleActions: roles.reduce((actions, role) => {
        actions[role] = getSolutionAction(role, task);
        return actions;
      }, {})
    },
    {
      name: "Evaluation and Refinement",
      description: "Evaluate solutions and refine as needed",
      roleActions: roles.reduce((actions, role) => {
        actions[role] = getEvaluationAction(role, task);
        return actions;
      }, {})
    }
  ];

  // Select the appropriate number of steps based on settings
  const selectedSteps = protocolSteps.slice(0, settings.interactionSteps);

  // Add quality checks if enabled
  if (settings.qualityChecks) {
    selectedSteps.push({
      name: "Quality Assurance",
      description: "Check the quality and correctness of the solution",
      roleActions: roles.reduce((actions, role) => {
        actions[role] = getQualityCheckAction(role, task);
        return actions;
      }, {})
    });
  }

  // Generate the protocol
  let protocol = `
    Task Protocol: ${task}

    Participants: ${roles.join(', ')}

    Instructions:
    Follow this structured protocol to complete the task. Each participant should perform their specified actions in each step.
  `;

  // Add steps to the protocol based on format
  if (settings.outputFormat === "structured") {
    selectedSteps.forEach((step, index) => {
      protocol += `

      Step ${index + 1}: ${step.name}
      ${step.description}

      Participant Actions:
      ${Object.entries(step.roleActions).map(([role, action]) => `- ${role}: ${action}`).join('\n')}
      `;
    });
  }
  else if (settings.outputFormat === "narrative") {
    protocol += `

    Process Narrative:

    Begin by ${selectedSteps[0].description.toLowerCase()}. `;

    for (let i = 1; i < selectedSteps.length; i++) {
      protocol += `Then, ${selectedSteps[i].description.toLowerCase()}. `;
    }

    protocol += `

    Throughout this process, each participant should contribute as follows:
    `;

    roles.forEach(role => {
      protocol += `

      ${role}:
      ${selectedSteps.map((step, i) => `- In step ${i+1} (${step.name}): ${step.roleActions[role]}`).join('\n')}
      `;
    });
  }
  else {
    selectedSteps.forEach((step, index) => {
      protocol += `

      Step ${index + 1}: ${step.name}
      ${step.description}
      `;
    });

    protocol += `

    Participant Responsibilities:
    `;

    roles.forEach(role => {
      protocol += `

      ${role}:
      ${selectedSteps.map((step, i) => `- In step ${i+1} (${step.name}): ${step.roleActions[role]}`).join('\n')}
      `;
    });
  }

  // Add adaptation rules if enabled
  if (settings.adaptationRules) {
    protocol += `

    Adaptation Rules:
    - If new information emerges that changes the understanding of the task, revisit the Task Analysis step.
    - If proposed solutions are found to be inadequate, return to the Solution Development step.
    - If implementation challenges arise, adapt the Implementation Planning accordingly.
    - Throughout the process, document any deviations from the protocol and the reasons for them.
    `;
  }

  // Add final output guidelines
  protocol += `

  Final Output:
  Upon completion of the protocol, produce:
  1. A summary of the process followed
  2. The final solution or deliverable
  3. Key insights or lessons learned
  4. Any recommendations for future improvements
  `;

  return protocol;
}

// Helper functions (simplified for illustration)
function getAnalysisAction(role, task) {
  const actions = {
    "Expert": "Provide domain expertise to identify key components and challenges in the task.",
    "Facilitator": "Guide the discussion to ensure all aspects of the task are considered.",
    "Critic": "Identify potential issues, constraints, or blind spots in the task analysis.",
    "Researcher": "Gather background information and context relevant to the task.",
    "Implementer": "Assess practical aspects and implementation requirements of the task.",
    "User": "Share user needs and perspectives related to the task."
  };

  return actions[role] || `Contribute to the analysis of the task from a ${role} perspective.`;
}

function getInformationAction(role, task) {
  const actions = {
    "Expert": "Share specialized knowledge and identify key information sources.",
    "Facilitator": "Organize and synthesize the gathered information.",
    "Critic": "Evaluate the quality and relevance of the information.",
    "Researcher": "Conduct research and compile findings from various sources.",
    "Implementer": "Identify practical information needed for implementation.",
    "User": "Provide user context and requirements information."
  };

  return actions[role] || `Gather relevant information from a ${role} perspective.`;
}

function getSolutionAction(role, task) {
  const actions = {
    "Expert": "Develop solutions based on domain expertise.",
    "Facilitator": "Facilitate brainstorming and solution development.",
    "Critic": "Challenge solutions and identify potential weaknesses.",
    "Researcher": "Propose evidence-based solutions.",
    "Implementer": "Develop practical, implementable solutions.",
    "User": "Evaluate solutions from user perspective."
  };

  return actions[role] || `Develop solutions from a ${role} perspective.`;
}

function getEvaluationAction(role, task) {
  const actions = {
    "Expert": "Evaluate technical soundness of solutions.",
    "Facilitator": "Facilitate evaluation discussion and synthesis.",
    "Critic": "Conduct critical evaluation of all solutions.",
    "Researcher": "Evaluate solutions against research evidence.",
    "Implementer": "Assess practical feasibility of solutions.",
    "User": "Evaluate solutions based on user needs."
  };

  return actions[role] || `Evaluate solutions from a ${role} perspective.`;
}

function getQualityCheckAction(role, task) {
  const actions = {
    "Expert": "Verify technical quality and standards.",
    "Facilitator": "Ensure comprehensive quality review.",
    "Critic": "Identify any remaining issues or concerns.",
    "Researcher": "Verify evidence base and accuracy.",
    "Implementer": "Check implementation readiness.",
    "User": "Confirm user requirements are met."
  };

  return actions[role] || `Check quality from a ${role} perspective.`;
}

// Usage examples
const projectProtocol = generateTaskProtocol(
  "Design a mobile app for tracking personal carbon footprint",
  ["UX Designer", "Developer", "Environmental Expert", "User"],
  { interactionSteps: 5, outputFormat: "hybrid" }
);

const researchProtocol = generateTaskProtocol(
  "Investigate the effects of social media on teenage mental health",
  ["Researcher", "Psychologist", "Data Analyst", "Teenager"],
  { outputFormat: "narrative" }
);

关键概念:

• 协议生成: 基于角色动态创建工作流
• 角色特定操作: 为每个参与者定制行动
• 格式灵活性: 结构化、叙述式或混合输出

高级认知系统架构

基于这些编程模式,我们可以创建复杂的认知系统架构。

1. 分层问题解决系统

这种架构结合多个认知程序层次结构来处理复杂问题。

function hierarchicalProblemSolver(problem, options = {}) {
  // Default options
  const defaults = {
    maxDepth: 3,
    verificationEnabled: true,
    reflectionEnabled: true,
    adaptiveStrategy: true
  };

  // Merge defaults with provided options
  const settings = {...defaults, ...options};

  // Top-level system prompt
  const systemPrompt = `
    Task: Solve the following complex problem using a hierarchical approach.

    Problem: ${problem}

    Instructions:
    Approach this problem using the following hierarchical system:

    1. EXECUTIVE LEVEL: Strategic Planning
       - Analyze the overall problem structure
       - Decompose into sub-problems
       - Develop a solution strategy
       - Coordinate lower levels

    2. TACTICAL LEVEL: Sub-Problem Solving
       - For each sub-problem identified above:
         - Analyze the specific sub-problem
         - Apply appropriate solution methods
         - Verify sub-solutions
         - Pass results back to Executive Level

    3. OPERATIONAL LEVEL: Specific Calculations or Reasoning
       - Execute specific reasoning operations
       - Perform calculations or specific analyses
       - Implement fine-grained solution steps
       - Return detailed results to Tactical Level
  `;

  // Generate the executive level
  const executiveLevel = `
    EXECUTIVE LEVEL: Strategic Planning

    1. Problem Analysis:
       - What type of problem is this?
       - What are the key components or dimensions?
       - What is the ultimate goal or desired outcome?
       - What high-level approach would be most effective?

    2. Problem Decomposition:
       - Break down the main problem into 2-4 distinct sub-problems
       - Ensure sub-problems are:
         a) Simpler than the original problem
         b) Relatively independent
         c) Collectively comprehensive
       - For each sub-problem:
         a) Clearly state what needs to be solved
         b) Specify what information is needed
         c) Indicate solution criteria

    3. Solution Strategy:
       - Determine the sequence for addressing sub-problems
       - Identify dependencies between sub-problems
       - Allocate attention/resources to each sub-problem
       - Plan how to integrate sub-solutions

    4. Coordination Plan:
       - Establish how sub-solutions will be combined
       - Define criteria for successful integration
       - Specify verification methods for the complete solution

    After completing the Executive Level analysis, proceed to solve each sub-problem at the Tactical Level.
  `;

  // Generate the tactical level
  const tacticalLevel = `
    TACTICAL LEVEL: Sub-Problem Solving

    For each sub-problem identified at the Executive Level:

    1. Sub-Problem Analysis:
       - Clarify the specific goal of this sub-problem
       - Identify relevant information and constraints
       - Determine appropriate solution methods
       - Establish success criteria for this sub-problem

    2. Solution Development:
       - Apply the selected solution method
       - Break down into operational steps as needed
       - Delegate specific calculations to the Operational Level
       - Track progress toward the sub-problem goal

    3. Sub-Solution Verification:
       - Check that the solution meets the specified criteria
       - Verify that constraints are satisfied
       - Test with examples or edge cases if applicable
       - Identify any limitations or assumptions

    4. Integration Preparation:
       - Format the sub-solution for integration
       - Note any implications for other sub-problems
       - Highlight key insights or unexpected findings
       - Pass the verified sub-solution to the Executive Level

    After addressing all sub-problems, return to the Executive Level for integration.
  `;

  // Generate the operational level
  const operationalLevel = `
    OPERATIONAL LEVEL: Specific Calculations or Reasoning

    For each operation requested by the Tactical Level:

    1. Operation Setup:
       - Clarify the specific calculation or reasoning task
       - Identify all required inputs and parameters
       - Select the appropriate method or formula
       - Prepare the necessary steps

    2. Execution:
       - Perform the calculation or reasoning steps
       - Show all work in detail
       - Track intermediate results
       - Apply appropriate precision and notation

    3. Verification:
       - Check for calculation errors
       - Verify dimensional consistency
       - Ensure the result makes sense in context
       - Perform sanity checks on the outcome

    4. Result Reporting:
       - Format the result clearly
       - Include relevant units or qualifiers
       - Note any caveats or limitations
       - Return the result to the Tactical Level
  `;

  // Add verification layer if enabled
  let verificationLayer = "";
  if (settings.verificationEnabled) {
    verificationLayer = `
      VERIFICATION LEVEL: Comprehensive Solution Verification

      After integrating all sub-solutions at the Executive Level:

      1. Consistency Check:
         - Ensure all components work together coherently
         - Verify that no contradictions exist between sub-solutions
         - Check that all problem constraints are satisfied

      2. Completeness Verification:
         - Confirm that all aspects of the original problem are addressed
         - Identify any gaps or unresolved elements
         - Ensure the solution fully answers what was asked

      3. Validity Testing:
         - Test the complete solution with examples if applicable
         - Consider edge cases or boundary conditions
         - Verify that the solution holds under various scenarios

      4. Quality Assessment:
         - Evaluate the elegance and efficiency of the solution
         - Consider alternative approaches that might be superior
         - Identify any simplifications or optimizations

      If any issues are found, return to the appropriate level for corrections.
    `;
  }

  // Add reflection layer if enabled
  let reflectionLayer = "";
  if (settings.reflectionEnabled) {
    reflectionLayer = `
      REFLECTION LEVEL: Meta-Cognitive Analysis

      After completing the solution process:

      1. Approach Evaluation:
         - Assess the effectiveness of the problem-solving approach
         - Identify what worked well and what could be improved
         - Consider alternative strategies that might have been more effective

      2. Knowledge Gaps:
         - Identify any areas where additional knowledge would have been helpful
         - Note any assumptions made due to incomplete information
         - Suggest how these gaps might be addressed in future

      3. Insight Extraction:
         - Identify key insights gained from solving this problem
         - Note any generalizable principles or patterns discovered
         - Consider how these insights might apply to similar problems

      4. Learning Integration:
         - Summarize the main lessons learned
         - Suggest how the approach might be refined for similar problems
         - Identify transferable strategies for different problem types
    `;
  }

  // Add adaptive strategy if enabled
  let adaptiveStrategy = "";
  if (settings.adaptiveStrategy) {
    adaptiveStrategy = `
      ADAPTIVE STRATEGY RULES:

      Throughout the problem-solving process, apply these adaptive rules:

      1. If a sub-problem proves more complex than anticipated:
         - Further decompose it into smaller sub-problems
         - Adjust the hierarchical structure accordingly
         - Allocate additional attention to this branch

      2. If integration reveals conflicts between sub-solutions:
         - Identify the source of the conflict
         - Revisit the relevant sub-problems with additional constraints
         - Develop a resolution approach at the Executive Level

      3. If verification reveals issues with the complete solution:
         - Trace the issue to the appropriate level
         - Apply targeted corrections rather than starting over
         - Re-verify the solution after corrections

      4. If new information or insights emerge during the process:
         - Evaluate their impact on the current approach
         - Incorporate relevant information at the appropriate level
         - Adjust the strategy if necessary

      These rules allow the system to adapt dynamically to challenges encountered during problem-solving.
    `;
  }

  // Construct the complete hierarchical problem-solving prompt
  const completePrompt = `
    ${systemPrompt}

    ${executiveLevel}

    ${tacticalLevel}

    ${operationalLevel}

    ${verificationLayer}

    ${reflectionLayer}

    ${adaptiveStrategy}

    Please solve the problem following this hierarchical approach, clearly indicating which level you are operating at during each phase of the solution process.

    Begin by analyzing the problem at the Executive Level.
  `;

  return completePrompt;
}

// Usage example
const complexProblemPrompt = hierarchicalProblemSolver(
  "Design a sustainable urban transportation system that reduces carbon emissions by 30% while improving commute times and accessibility for all residents.",
  { maxDepth: 4, reflectionEnabled: true }
);

const mathProblemPrompt = hierarchicalProblemSolver(
  "Find all solutions to the system of equations: 2x² + y² = 18, xy = 4",
  { maxDepth: 3, adaptiveStrategy: false }
);

关键概念:

• 层次结构: 执行、战术和操作层
• 自适应规则: 根据需要动态调整策略
• 集成验证: 跨层次检查一致性

2. 协作多智能体架构

这种架构编排多个专门智能体协同工作来解决复杂问题。

function collaborativeMultiAgentSystem(task, agentRoles = null, options = {}) {
  // Default options
  const defaults = {
    maxIterations: 3,
    collaborationMode: "sequential", // Can be "sequential", "parallel", or "hybrid"
    outputFormat: "comprehensive",  // Can be "comprehensive", "concise", or "stepwise"
    facilitatorEnabled: true
  };

  // Merge defaults with provided options
  const settings = {...defaults, ...options};

  // Default agent roles if not provided
  if (!agentRoles) {
    agentRoles = [
      {
        name: "Analyst",
        expertise: "Problem analysis and decomposition",
        responsibilities: "Breaking down the task, identifying key components and requirements"
      },
      {
        name: "Researcher",
        expertise: "Information gathering and synthesis",
        responsibilities: "Collecting relevant information, identifying key sources and facts"
      },
      {
        name: "Creator",
        expertise: "Solution generation and innovation",
        responsibilities: "Developing creative solutions, exploring alternatives"
      },
      {
        name: "Critic",
        expertise: "Evaluation and refinement",
        responsibilities: "Identifying flaws, suggesting improvements, testing solutions"
      },
      {
        name: "Integrator",
        expertise: "Synthesis and coherence",
        responsibilities: "Combining insights, ensuring consistency, creating final output"
      }
    ];
  }

  // Build the system prompt
  const systemPrompt = `
    Task: Solve the following complex task using a collaborative multi-agent approach.

    Task Description: ${task}

    Instructions:
    You will simulate a collaborative problem-solving system with multiple specialized agents working together.
    Each agent has specific expertise and responsibilities. The agents will work through the task in a structured way.
  `;

  // Build the agent descriptions
  let agentDescriptions = `
    Agent Profiles:
  `;

  agentRoles.forEach((agent, index) => {
    agentDescriptions += `
    Agent ${index + 1}: ${agent.name}
    - Expertise: ${agent.expertise}
    - Responsibilities: ${agent.responsibilities}
    `;
  });

  // Build the facilitator description if enabled
  let facilitatorDescription = "";
  if (settings.facilitatorEnabled) {
    facilitatorDescription = `
    Facilitator:
    The Facilitator orchestrates the collaboration, ensures all agents contribute effectively,
    identifies gaps or conflicts, and guides the process toward successful completion of the task.
    The Facilitator does not contribute content but focuses on process.
    `;
  }

  // Build the collaboration process based on the selected mode
  let collaborationProcess = "";

  if (settings.collaborationMode === "sequential") {
    collaborationProcess = `
    Collaboration Process (Sequential Mode):

    The agents will work on the task in sequence, with each agent building on the work of previous agents.

    Process Flow:
    ${agentRoles.map((agent, i) => `${i+1}. ${agent.name} contribution`).join('\n')}
    ${settings.facilitatorEnabled ? `${agentRoles.length+1}. Facilitator synthesis and guidance` : ''}

    This sequence will repeat for up to ${settings.maxIterations} iterations or until the task is completed satisfactorily.
    In each iteration, agents should build upon and refine the work from previous iterations.
    `;
  }
  else if (settings.collaborationMode === "parallel") {
    collaborationProcess = `
    Collaboration Process (Parallel Mode):

    The agents will work on the task simultaneously, each contributing from their area of expertise.

    Process Flow:
    1. All agents analyze the task from their perspective
    2. All agents contribute their insights simultaneously
    ${settings.facilitatorEnabled ? '3. Facilitator synthesizes contributions and identifies areas for further work' : '3. Collective review of all contributions'}
    4. Integration of all perspectives into a coherent solution

    This parallel process will repeat for up to ${settings.maxIterations} iterations or until the task is completed satisfactorily.
    In each iteration, agents should refine their contributions based on the collective work.
    `;
  }
  else {
    collaborationProcess = `
    Collaboration Process (Hybrid Mode):

    The agents will work in a flexible manner, combining sequential and parallel work as appropriate.

    Process Flow:
    1. Initial parallel analysis by all agents
    2. Sequential deep dives based on identified key areas
    3. Parallel refinement of solutions
    4. Sequential [注: 源文件在此处截断]
    `;
  }

  // Construct the complete multi-agent prompt
  const completePrompt = `
    ${systemPrompt}

    ${agentDescriptions}

    ${facilitatorDescription}

    ${collaborationProcess}

    Please simulate this multi-agent collaboration to complete the task, clearly indicating which agent is speaking or acting at each step.
  `;

  return completePrompt;
}

// Usage examples
const designTaskPrompt = collaborativeMultiAgentSystem(
  "Design a mobile app for tracking personal carbon footprint",
  [
    { name: "UX Designer", expertise: "User experience design", responsibilities: "Creating intuitive interfaces" },
    { name: "Developer", expertise: "Software development", responsibilities: "Technical implementation" },
    { name: "Environmental Expert", expertise: "Carbon footprint analysis", responsibilities: "Ensuring accurate calculations" },
    { name: "User Representative", expertise: "User needs", responsibilities: "Representing user perspective" }
  ],
  { collaborationMode: "hybrid", maxIterations: 2 }
);

const researchTaskPrompt = collaborativeMultiAgentSystem(
  "Investigate the effects of social media on teenage mental health",
  null, // Use default agents
  { collaborationMode: "sequential", outputFormat: "comprehensive" }
);

关键概念:

• 多智能体协作: 专门智能体协同工作
• 协作模式: 顺序、并行或混合工作流
• 角色专业化: 每个智能体有特定的专业知识

实现高级认知程序

高级认知程序可以通过多种方式实现:

1. 模块化库结构

// Advanced Cognitive Programs Library
const AdvancedCognitivePrograms = {
  higherOrder: {
    applyReasoningStrategy,
    withExampleGeneration,
    withAlternativePerspectives
  },

  selfImproving: {
    selfImprovingReasoner,
    customSelfImprovingReasoner
  },

  metaProgramming: {
    generateSpecializedReasoner,
    dynamicComputationalReasoning
  },

  systemArchitectures: {
    hierarchicalProblemSolver,
    collaborativeMultiAgentSystem,
    generateTaskProtocol
  }
};

// Usage
const prompt = AdvancedCognitivePrograms.metaProgramming.generateSpecializedReasoner("mathematics", "advanced");

2. 链式组合

// Compose multiple advanced programs
const advancedSolver =
  withAlternativePerspectives(
    withExampleGeneration(
      selfImprovingReasoner
    )
  );

// Apply to complex problem
const prompt = advancedSolver(
  "How might quantum computing impact cryptography and cybersecurity?",
  2, // iterations
  { enableReflection: true }
);

3. 配置驱动的程序生成

const programConfig = {
  domain: "ethics",
  complexity: "advanced",
  features: ["self-improvement", "alternative-perspectives", "examples"],
  iterations: 3
};

const customProgram = buildProgramFromConfig(programConfig);
const prompt = customProgram("Should AI systems be granted legal personhood?");

性能优化

高级认知程序需要仔细的性能优化:

1. Token效率: 平衡详细程度与token使用
2. 缓存策略: 重用常见的程序组件
3. 渐进式增强: 从简单程序开始,根据需要添加复杂性
4. 基准测试: 测量不同配置的有效性

下一步

• 在实际项目中实践这些高级模式
• 为特定领域创建自定义变体
• 组合多个模式以应对独特挑战
• 探索 基础程序 作为构建基础
• 查看完整实现库以获取代码示例

---

总结

高级认知程序通过以下方式扩展基本模式:

1. 高阶抽象: 操作其他程序的程序
2. 动态适应: 基于上下文调整行为的程序
3. 自我改进: 完善自己输出的程序
4. 元编程: 生成其他程序的程序
5. 系统架构: 组合多个程序的复杂系统

这些高级技术使我们能够创建复杂、适应性强且强大的认知系统,能够处理各种推理任务。

注: 源文件在"4. Sequential"处不完整截断,译文保持了与原文相同的截断点。