As someone who has spent quite some time pondering cognitive architectures and their capabilities, I’ve come to recognize that there are certain limitations to existing models that can’t be ignored. While these architectures have been invaluable in helping us understand how the brain processes information and how we might replicate that process in artificial intelligence systems, there are still some areas where they fall short. I deeply believe that CAs are on a good way to become the next big thing on the way to “real” AI, but want to use this article to explicitly point out the shortcomings. I focus in the examples on SOAR, ACT-R and PSI theory, but the limitations apply more or less similarly to all the other CAs out there to date.
Coping with Complexity
One of the most significant limitations of existing cognitive architectures is their inability to account for the complexity and nuance of real-world situations. Many of these models are designed to operate in simplified, controlled environments that don’t accurately reflect the chaos and unpredictability of the world around us. This means that while these architectures may be effective in certain applications, they can struggle when faced with more complex scenarios.
For example one major limitation of SOAR is its reliance on production rules, which are the basic building blocks of its knowledge representation. These rules are used to specify how the system should respond to different stimuli, and they are structured in the form of IF-THEN statements. However, production rules can be inflexible and can lead to combinatorial explosion, making it difficult for SOAR to deal with complex environments.
Taken from a different perspective, one of the main limitations of PSI theory is its complexity. The architecture is highly abstract and incorporates many different cognitive processes and sub-processes, making it difficult to understand and implement. In addition, the complexity of the architecture can make it difficult to apply in real-world scenarios where computational efficiency is a concern.
Most cognitive architectures are designed to operate within a static and well-defined environment, where the rules of the game are known in advance. However, the real world is highly dynamic, and an agent’s behavior needs to adapt to constantly changing circumstances. Cognitive architectures that cannot deal with these dynamic situations can quickly become overwhelmed and fail to provide adequate responses.
SOAR for example has limited support for handling uncertainty and probabilistic reasoning. In real-world environments, there is often uncertainty in the input data, and the system needs to make probabilistic inferences to deal with this uncertainty. SOAR, however, does not have built-in mechanisms for probabilistic reasoning, and this can limit its ability to handle complex, real-world scenarios.
Learning from Experience
Another limitation of existing cognitive architectures is their inability to effectively learn from experience. While these models are often built around the idea of adapting to new situations and stimuli, they can sometimes struggle to do so in practice. This is particularly true when it comes to tasks that require a high degree of flexibility or creativity – tasks that humans might excel at, but that cognitive architectures may struggle with.
While some architectures have mechanisms for learning, such as reinforcement learning or Bayesian inference, they often require significant amounts of data to be effective. Human beings, on the other hand, are able to learn from a relatively small number of experiences, allowing us to adapt quickly to new situations.
As an example, a limitation of SOAR is its lack of support for continuous learning. SOAR is designed to operate in a closed world assumption, meaning that it assumes all possible stimuli and responses are specified in advance. In real-world applications, however, it is difficult to specify all possible stimuli, and new situations may arise that require the system to adapt its behavior. SOAR does not provide an efficient way to learn from new experiences and update its knowledge.
Same with PSI theory: the architecture has limited support for learning from experience. While the architecture is designed to incorporate learning and adaptation, the learning mechanisms are not well-defined and can be difficult to implement. In addition, the architecture does not support unsupervised learning, which can be limiting in applications where labeled training data is not readily available.
A related limitation is the fact that many existing cognitive architectures are designed with specific tasks or applications in mind. While this can make them highly effective at those particular tasks, it can also limit their versatility and adaptability in other contexts. This can be problematic in situations where the architecture needs to operate in a more open-ended or exploratory way.
For example, a cognitive architecture designed to play chess may perform extremely well at the game, but it may struggle to generalize its knowledge and skills to other tasks, such as playing checkers. Similarly, a cognitive architecture designed to recognize objects in images may perform well on specific datasets, but may not be able to accurately recognize objects in new contexts or environments.
One of the primary limitations for example of ACT-R is its lack of flexibility in handling new problem domains. ACT-R’s core cognitive mechanisms are hard-coded and cannot be easily modified or adapted to new tasks or problem domains. This can make it difficult to use ACT-R in real-world applications that require flexible, adaptive cognitive architectures.
Then, there is the issue of scalability. While many existing cognitive architectures have proven effective in small-scale applications, they can struggle when it comes to larger, more complex scenarios. This is particularly true when it comes to real-world applications like autonomous driving or robotics, where the architecture needs to be able to process vast amounts of data and make complex decisions in real-time. Many existing architectures are limited in their ability to handle large-scale problems, such as natural language processing or computer vision. As the complexity of the problem increases, the demands on the architecture also increase, making it difficult to achieve high levels of performance.
SOAR and ACT-R for example have been criticized for being too focused on symbolic processing, which can limit its ability to handle perceptual information. The system relies heavily on symbolic representations, and this can make it difficult to integrate perceptual information such as images and sound.
More generally, ACT-R is suffering from a lack of scalability. While ACT-R is capable of modeling complex cognitive tasks, it can become computationally expensive and difficult to scale up to larger, more complex problem domains. This is because ACT-R’s symbolic processing requires a lot of computational resources, which can make it impractical for some applications.
In PSI theory, as another example, a major limitation is its lack of a clear separation between high-level and low-level cognitive processes. While the architecture is intended to be modular, the various modules are not well-defined, which can make it difficult to identify the underlying cognitive processes that are responsible for a given behavior.
(MicroPSI, a variant of PSI theory, attempts to address some of these limitations by providing a more concrete implementation of the architecture. MicroPSI is designed to be more modular, with well-defined modules that correspond to specific cognitive processes. The architecture is also designed to be more computationally efficient, making it more suitable for real-world applications.)
The (Internal) Exploration-Exploitation Trade-Off
Another challenge in developing cognitive architectures is the need to balance flexibility and efficiency. In order to handle complex tasks, a cognitive architecture must be flexible enough to handle a wide range of inputs and outputs, while also being efficient enough to process information quickly and accurately. Achieving this balance is a major challenge in the field, and requires careful consideration of the specific requirements of the system being developed.
Another limitation of existing cognitive architectures is their lack of ability to handle multiple tasks simultaneously. Most current cognitive architectures are designed to perform a specific set of tasks, and often require significant modifications to be made in order to perform additional tasks. This can make it difficult to develop versatile systems that can handle multiple tasks effectively.
A fundamental assumption for many researchers is that a full-blown CA should also model human social behavior and emotion processing (I don’t share that view, but likely will elaborate about it in a future article). A limitation then is the difficulty in capturing the social aspects of cognition. Human cognition is inherently social, and we are constantly interacting with others in a variety of contexts. However, most cognitive architectures do not have a mechanism for modeling these social interactions, which limits their applicability in real-world scenarios.
In addition, cognitive architectures often struggle with capturing the full range of human emotions and motivations. While some architectures have incorporated basic emotions into their models, more complex emotions and motivations are often difficult to represent. This is due in part to the fact that emotions and motivations are highly subjective and dependent on individual experience, making them difficult to quantify and model.
Overall, while existing cognitive architectures have been invaluable in advancing our understanding of the brain and developing new AI systems, it’s clear that they have their limitations. As we continue to explore the possibilities of artificial intelligence and push the boundaries of what these systems can do, it will be important to keep these limitations in mind and work towards developing more versatile, adaptable architectures that can effectively handle the complexities of the real world.