Que faire si votre algorithme robotique manque de techniques de raisonnement logique ?

Identify, Inconsistent Behaviour, Errors, Poor performance on complex task, Evaluation against Known Logical Problems, to name few. Identify where exactly the pain area is. Once identified do thorough study on deductive reasoning, inductive reasoning, abductive reasoning, symbolic logic, fuzzy logic, probabilistic reasoning etc. which is Imp. In robotics and AI. Examine existing algorithm you feel there is lacking on logical reasoning. Understand the algorithm relies on heuristic or log logical approach. By systematically addressing these points & continuously refining the algorithm based on feedback and evaluation, one can improve its logical reasoning capabilities and enhance its overall performance in various tasks and scenarios.

Begin by clearly defining the problem domain and understanding the specific tasks or challenges that your robotic algorithm is intended to address. Identify the types of logical reasoning required to effectively navigate and solve problems within this domain, such as deductive reasoning, inductive reasoning, or abductive reasoning.

Before you write your logical reasoning algorithm for the robot, define the problem domain and the solution. Identify the robot’s goals, tasks, environmental considerations, and constraints. Identify available information and types of information, as well as possible consequences and actions. This process helps you narrow down the relevant facts and rules for your algorithm, reducing complexity and ambiguity. A clear identification of the problem area is essential for effective and focused logical thinking in robotic design.

State of the art, autonomous, Robotics coding is an integral part of an interdisciplinary subject, which relates to mechanical, electrical and software analysis. It is done at conceptual, detailed and applied levels of product development phases, at their respective proof of concept, prototype development and acceptance testing complexities. Mechanical coding relates to statics for strength analysis, kinematics for motion analysis and Newton Euler dynamics analysis to determine the required motor torque to meet the robot kinematics under inertia and environmental loads. Electrical coding relates to sensor feedback and PID motion control in linear and known environment, or AI/ML, NN motion control for non linear and random environment.

Defining KPI before starting to implement a new solution allow you to compare your results later. Asking peers still remain in my opinion one of the preferred solution.

Select the appropriate logic framework or formalism that aligns with the problem domain and the nature of the reasoning tasks involved. Consider options such as propositional logic, first-order logic, modal logic, or fuzzy logic, depending on the complexity and nuances of the problem space. Choose a logic formalism that provides the expressive power and flexibility needed to represent and manipulate the relevant knowledge and constraints.

Choose the appropriate logic framework based on your problem domain and level of abstraction. Propositional arguments are valid for binary statements such as "the door is open." Predicate logic Handle statements with variables such as "All dogs are mammals". Ambiguity deals with uncertainty, for example, "The temperature is hot." Modal arguments express possibilities or obligations, such as "maybe the cat is in bed." Choose the program that matches the complexity of your problem and the algorithm requirements that will give you the best results.

Develop or integrate a logic inference engine into your robotic algorithm to perform automated reasoning based on the selected logic framework. Design the inference engine to effectively derive logical conclusions, make decisions, or solve problems by applying inference rules, logical axioms, and domain-specific knowledge encoded in the system. Implement efficient algorithms for logical inference that can handle the computational complexity of reasoning tasks in real-time or near-real-time scenarios.

Choose the right logic framework and use the logic inference engine, which is important for logical reasoning algorithms. This engine applies reasoning rules to facts and draws conclusions. Methods such as resolution, forward series, backward series, or tables offer a variety of statistical methods. Choose the method that best suits your argumentation process and problem area for efficiency and reliability. Carefully test and troubleshoot the engine to ensure that it handles a variety of conditions and inputs correctly.

Build and maintain a comprehensive knowledge base that encapsulates the domain-specific knowledge, rules, facts, and relationships relevant to the problem-solving tasks of the robotic algorithm. Populate the knowledge base with structured and semantically rich data sources, ontologies, rulesets, or knowledge graphs that capture the logical dependencies and constraints inherent in the problem domain. Continuously update and refine the knowledge base to incorporate new information, insights, or feedback obtained during the operation of the robotic system.

Keep improving your formal reasoning skills to adapt algorithms of logical reasoning. The knowledge base contains facts and rules that are critical to the performance of the heuristic machine. Regularly update with new data, feedback, or insights. Use techniques such as data learning, natural language extraction, combining features, or paradox-based analysis to increase knowledge base. These techniques ensure that logical reasoning is accurate and scalable, supporting more efficient decision-making in AI systems.

Evaluate the effectiveness, accuracy, and robustness of the logic reasoning output produced by the robotic algorithm through rigorous testing, validation, and verification procedures. Assess the algorithm's ability to generate logical conclusions, make decisions, or solve problems in various scenarios and under different conditions. Use performance metrics, test suites, and benchmarking protocols to quantitatively measure the quality and reliability of the logic reasoning capabilities and identify areas for improvement.

Evaluate the output of your logical reasoning process for effectiveness and relevance. Compare results with expected results for consistency, accuracy, and relevance. Observe the effect on the robot’s actions and goals, to ensure optimal actions. Monitor algorithm performance and errors, identify areas for improvement or change. This iterative analysis process ensures continuous refinement of your logical reasoning, increasing its accuracy and usefulness in guiding the robot’s decision-making processes

When I am making this it will not lack any of such things. Or u cannot imagine u are complete without this logical reasoning and thinking.