Connectionist Expert System

Notes:

A connectionist expert system (CES) is a type of artificial intelligence (AI) system that uses a connectionist approach to solve complex problems. It is a combination of a traditional expert system, which uses a set of rules or algorithms to solve problems, and a connectionist model, which uses a network of interconnected nodes or neurons to process information.

CESs are used for a variety of tasks, including decision making, prediction, and classification. They are particularly useful for solving complex problems that require a high degree of flexibility and adaptability. For example, a CES could be used to diagnose and treat medical conditions, to predict stock prices, or to identify and classify objects in images.

CESs are able to solve complex problems by combining the strengths of both expert systems and connectionist models. The expert system component provides a set of rules or algorithms that the CES can use to solve the problem, while the connectionist component allows the CES to learn from data and adapt to new situations. This allows the CES to solve complex problems more effectively than either an expert system or a connectionist model alone.

Engineering a robot brain into a connectionist expert system (CES) involves integrating artificial intelligence (AI) and machine learning algorithms into the robot’s hardware and software. Here are some steps that could be involved in the process:

1. Determine the desired capabilities and functions of the robot: The first step in engineering a robot brain into a CES is to define the specific capabilities and functions that the robot should be able to perform. This will help to determine the types of AI and machine learning algorithms that will be needed to support these functions.
2. Select and integrate appropriate AI and machine learning algorithms: Once the desired capabilities and functions of the robot have been defined, the next step is to select and integrate the appropriate AI and machine learning algorithms to enable the robot to perform these tasks. This may involve selecting and implementing algorithms such as neural networks, decision trees, or support vector machines.
3. Train and test the robot’s AI and machine learning algorithms: Once the AI and machine learning algorithms have been integrated into the robot, they will need to be trained on appropriate data sets to enable the robot to perform the desired tasks. This may involve using techniques such as supervised learning or reinforcement learning to teach the robot to recognize patterns, make decisions, or perform actions based on its inputs.
4. Integrate the robot’s AI and machine learning algorithms into its hardware and software: Once the AI and machine learning algorithms have been trained and tested, the next step is to integrate them into the robot’s hardware and software. This may involve designing and building custom hardware or software components to support the robot’s AI and machine learning functions, or integrating existing components into the robot’s design.

Here are some examples of brain functions that correspond to existing software:

1. Memory: Software such as databases and data storage systems can be used to store and retrieve data, much like the brain’s memory functions.
2. Learning: Machine learning algorithms and other types of software can be used to learn and adapt based on data inputs, similar to how the brain learns and adapts to new experiences.
3. Decision-making: Software such as expert systems and decision support systems can be used to analyze data and make decisions based on predetermined rules or algorithms, similar to how the brain processes information and makes decisions.
4. Language processing: Natural language processing (NLP) software can be used to analyze and interpret human language, similar to how the brain processes language.
5. Perception: Software such as image recognition and object detection systems can be used to analyze sensory data and perceive the environment, similar to how the brain processes visual and other sensory information.

Wikipedia:

• Connectionist Expert System

See also:

100 Best Expert System Videos | Conversational Expert | Diagnostic Expert Systems | Expert Systems | JESS (Java Expert System Shell) 2011 | Medical Expert Systems

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Automated generation of connectionist expert systems for problems involving noise and redundancy SI Gallant – arXiv preprint arXiv:1304.2735, 2013 – arxiv.org Abstract: When creating an expert system, the most difficult and expensive task is constructing a knowledge base. This is particularly true if the problem involves noisy data and redundant measurements. This paper shows how to modify the MACIE process for … Cited by 13 Related articles All 4 versions

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Connectionist expert system to diagnose neck and arm pain ST Selvi – Defence Science Journal, 2013 – publications.drdo.gov.in Abstract A connectionist expert system (CES) called BIONET aimed at assisting physicians in the diagnosis of diseases, such as neck and arm pain has been proposed. BIONET is an artificial network or connectionist network model capable of classifying diseases. Need for … Related articles All 4 versions

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