Mind in Motion: Understanding Practical Reasoning from Aristotle to AI

The Central Role of Action in Intelligence: Practical Reasoning from Aristotle to AI

The concept of intelligence has fascinated philosophers, scientists, and AI researchers for centuries. One fundamental aspect of intelligence is the ability to act effectively in the world. This idea is grounded in practical reasoning—the process of thinking about what to do, rather than merely what to believe. Practical reasoning involves deliberating about actions and taking steps that lead to the desired outcomes, a concept that has evolved through history, from Aristotle’s philosophical insights to the development of artificial intelligence (AI) in the modern world.

This blog explores how practical reasoning has shaped our understanding of intelligence, particularly focusing on Aristotle’s Nicomachean Ethics and John McCarthy’s 1985 paper, Programs with Common Sense. It also delves into the implications of practical reasoning for both human cognition and AI systems, illustrating how action is central to any notion of intelligence.

Aristotle’s Concept of Practical Reasoning in Nicomachean Ethics

In his Nicomachean Ethics, Aristotle introduced the idea that intelligence is fundamentally tied to action. For Aristotle, intelligence is not only about abstract thought or theoretical reasoning; it also involves the ability to make decisions about how to act in various situations, which he called practical wisdom (phronesis).

Aristotle’s practical reasoning is driven by goals and ends—specifically, how humans can live a good life. In this context, intelligence is the capacity to reason about means to ends, determining the best course of action in a given situation to achieve a desired outcome, whether that outcome is material success, moral virtue, or personal happiness. Key aspects of Aristotle’s approach to practical reasoning include:

Action-Oriented Intelligence: Intelligence is about more than knowing facts; it’s about knowing how to use those facts to achieve goals.
Deliberation and Choice: Practical reasoning involves deliberating about possible actions and making choices based on the perceived best option.
Ethical Considerations: Practical reasoning is not purely mechanistic; it involves moral reasoning and evaluating the ethical implications of actions.

This emphasis on acting wisely in the world has continued to influence discussions about intelligence, particularly when considering autonomous decision-making in machines and AI.

McCarthy’s 1985 Paper: Programs with Common Sense

The 20th century saw a shift in the way practical reasoning was applied—not just to human intelligence but to machines. John McCarthy, one of the founders of artificial intelligence, explored how to endow machines with common sense reasoning, which is essential for intelligent action in real-world environments.

In his 1985 paper, Programs with Common Sense, McCarthy argued that for a program to be truly intelligent, it must be able to reason about actions and the consequences of those actions in a way that mirrors human practical reasoning. McCarthy envisioned an AI system that could:

Understand and predict the effects of actions: A system with practical reasoning capabilities would not only understand what actions are available but also predict the consequences of those actions.
Plan actions to achieve goals: Similar to Aristotle’s notion of means-to-end reasoning, McCarthy’s vision of AI involved the ability to plan sequences of actions that lead to a desired outcome.
Adapt to new situations: True intelligence requires the ability to apply general knowledge to specific, novel situations, a key aspect of McCarthy’s work on formalizing common sense.

In McCarthy’s view, intelligence isn’t just about solving static problems; it’s about responding dynamically to a changing world. This view of AI as active agents who must reason and act in complex environments built upon the foundations of practical reasoning first articulated by Aristotle.

Practical Reasoning in Human Intelligence: A Cognitive and Evolutionary Perspective

From a cognitive science and evolutionary standpoint, practical reasoning plays a critical role in human survival and success. The ability to make decisions, plan, and execute actions has been central to human evolution. Here’s why:

Problem Solving and Adaptation: Practical reasoning allows humans to navigate challenges, from hunting and gathering to managing social relationships. This has evolved into more complex forms of problem-solving, such as business decision-making, medical diagnostics, and engineering.
Cognitive Load and Action: Human brains are wired to handle limited amounts of information at a time. Practical reasoning helps prioritize relevant information for decision-making, allowing us to focus on actions that directly affect our goals.
Social Intelligence: Many human actions are social in nature. Practical reasoning involves social cognition, understanding others’ intentions, cooperating, and competing in ways that help achieve personal or collective objectives.
Emotions and Practical Reasoning: Emotions often guide practical reasoning, influencing decisions by adding emotional weight to certain outcomes. This is a uniquely human feature, as AI lacks emotional processing, which sometimes leads to more mechanical decisions.
Ethical and Moral Reasoning: While AI can be programmed with rules, humans use practical reasoning to navigate moral dilemmas where there is no clear “right” answer, considering societal norms, personal values, and consequences.

Practical Reasoning in AI: Autonomous Systems and Robotics

In AI, the challenge of practical reasoning involves creating systems that can act autonomously in the real world. Unlike human reasoning, which is often influenced by emotions, cultural context, and moral judgments, AI systems are designed to process logical inferences and respond to situations based on pre-programmed rules or learned behaviors.

Planning and Action: Autonomous systems use algorithms to plan and carry out actions based on their understanding of the environment and desired goals. For instance, robotics often employs practical reasoning to navigate environments, manipulate objects, and achieve tasks.
AI and Common Sense: One of the hardest challenges in AI is incorporating common sense knowledge, which humans naturally use in practical reasoning. This includes understanding cause-and-effect relationships and reasoning about everyday scenarios, like knowing a cup will break if dropped.
Reinforcement Learning: In AI systems, practical reasoning is often implemented through reinforcement learning, where agents learn which actions lead to desired rewards over time. This method mirrors how humans learn from trial and error, although it lacks the emotional or ethical components that guide human action.

Challenges in AI Practical Reasoning: Can Machines Truly Understand?

Despite great strides in AI development, practical reasoning in machines still faces significant challenges:

Context Sensitivity: Humans are adept at understanding context and adjusting their actions based on subtle changes in the environment. AI, on the other hand, struggles with ambiguity and context-specific reasoning.
Moral and Ethical Decision-Making: AI lacks the ability to engage in moral reasoning in the way humans do. While machines can follow rules, they don’t understand the moral weight of their actions, which is a key component of human practical reasoning.
Creativity and Innovation: Human practical reasoning often involves creativity—coming up with novel solutions to problems. While AI can optimize based on data, its capacity for true creative thought is still limited.

Future of Practical Reasoning in AI and Human Collaboration

Looking forward, the future of practical reasoning will likely involve a combination of human-AI collaboration. Humans excel at creative problem-solving, moral reasoning, and social intelligence, while AI can handle vast amounts of data and make decisions with speed and precision.

Augmented Intelligence: Rather than replacing human reasoning, AI may be used to augment human decision-making, providing real-time suggestions, data analysis, and predictions that humans can then act on.
Ethical AI: As AI systems become more integrated into daily life, there will be an increasing focus on ensuring these systems can reason about the ethical implications of their actions.

To delve deeper into the central role of action in intelligence and expand the conversation on practical reasoning from basic to advanced perspectives, let’s explore additional facets from philosophy, cognitive science, artificial intelligence (AI), neuroscience, and the evolutionary function of action in intelligence. These new points build on, but do not repeat, the ideas already covered.

1. The Evolutionary Basis of Action-Oriented Intelligence

Survival and Adaptation Through Action:

Early human intelligence evolved primarily as a tool for survival, where quick, effective actions were critical for tasks such as hunting, gathering, and social interaction. The brain’s practical reasoning capabilities were shaped by the need to make fast decisions in the face of danger, complex environments, and resource acquisition.
Decision-making under uncertainty: Primitive humans had to act without complete information, honing their intelligence to balance risk and reward. This form of practical reasoning is still seen in both humans and AI, as they assess probabilities, make educated guesses, and act accordingly.

Problem-Solving as an Adaptive Advantage:

Practical reasoning in complex environments: Human intelligence evolved to allow not only reactive behaviors but also strategic, forward-thinking actions. This includes the ability to plan, predict outcomes, and adjust actions dynamically based on environmental changes. This mirrors AI’s use of dynamic decision trees and predictive algorithms.
Trial-and-error learning: Evolution has optimized the human brain for trial-and-error learning, where mistakes in actions lead to adjustments and better future decisions. Reinforcement learning in AI takes inspiration from this model, where systems learn which actions yield the highest reward.

2. Cognitive Foundations of Practical Reasoning

The Dual-Process Theory:

System 1 (Intuitive/Automatic) vs. System 2 (Deliberative): Human intelligence operates on two levels—System 1 handles automatic, fast, intuitive decision-making, often guiding immediate actions based on heuristics and learned patterns. System 2 is more reflective and deliberate, responsible for slower, conscious reasoning when complex actions are required. This dual-system approach is central to understanding practical reasoning in humans.
Cognitive load management: Effective practical reasoning requires managing cognitive load, where the brain prioritizes information relevant to immediate action. Cognitive scientists have explored how this capacity influences both intelligent actions and human limitations. AI attempts to mimic this by creating layers of processing where some inputs are handled automatically while others require deeper analysis.

The Role of Memory in Practical Reasoning:

Working memory is critical for holding and manipulating information when deciding on a course of action. It allows for the evaluation of options, simulation of possible outcomes, and the synthesis of past experiences to guide current decisions.
Procedural memory stores the knowledge of how to perform actions, often without conscious thought. This is important for skilled activities like driving or playing an instrument, where actions are carried out efficiently without continuous conscious reasoning.

3. Neuroscience of Practical Reasoning and Action

Neural Mechanisms of Decision-Making:

The brain’s prefrontal cortex plays a key role in practical reasoning, responsible for executive functions such as planning, problem-solving, and regulating behaviors. It helps weigh possible actions against their outcomes.
Basal ganglia and motor cortices: These brain regions are involved in coordinating actions, especially those that have been learned and become habitual. They serve as the neural pathways for translating thought into physical movement or action, reflecting the brain’s ability to operationalize reasoning.

Neuroplasticity and Adaptive Intelligence:

Neuroplasticity—the brain’s ability to reorganize and form new neural connections—allows humans to continuously improve their practical reasoning capabilities. For example, learning a new skill involves the creation of new neural pathways, which over time become more efficient, reducing the cognitive load required to perform that skill.
Adaptive action: Neuroplasticity also supports the brain’s ability to adapt its reasoning based on feedback from previous actions. AI systems that use reinforcement learning emulate this process, optimizing actions based on rewards received for past decisions.

4. AI’s Attempt to Replicate Practical Reasoning

Context-Aware AI:

One major challenge for AI in replicating human practical reasoning is context awareness. While human intelligence can effortlessly take into account contextual factors, such as social cues, environmental changes, and ethical considerations, AI systems struggle to adapt when the parameters of a situation change unpredictably.
Multimodal reasoning: Modern AI is working on multimodal learning, where systems process information from various input sources (visual, auditory, textual) to make better-informed decisions. This is an advanced form of practical reasoning that moves beyond rule-based action into more flexible, adaptive behavior.

Temporal Planning in AI:

Temporal reasoning refers to the ability of AI systems to not only consider immediate actions but also think ahead—similar to humans planning for future events. This involves creating action sequences that take into account the timing of each step, anticipating future needs, and adjusting accordingly.
Hierarchical task planning: Some advanced AI systems break down complex actions into sub-actions, creating hierarchical structures similar to how human brains organize tasks into manageable parts. This is essential for long-term, strategic decision-making in AI, particularly in autonomous systems like self-driving cars or robotic assistants.

5. Cognitive and Emotional Factors in Human Practical Reasoning

The Role of Emotion in Decision-Making:

Unlike AI, human practical reasoning is heavily influenced by emotions. Emotions provide feedback on past actions (regret, satisfaction) and serve as motivational forces for future actions (fear, excitement). This emotional guidance is essential for human decision-making but difficult for AI to replicate.
Affective computing: Researchers in AI are exploring how to incorporate emotional intelligence into systems so that machines can better predict and react to human emotional states when interacting in real-world environments.

Individual Differences in Practical Reasoning:

Genetic factors: Variations in brain structure and function, influenced by genetics, can lead to significant differences in people’s ability to engage in practical reasoning. Some individuals may have greater capacity for executive function, allowing them to plan and carry out actions more efficiently.
Environmental influence: Upbringing, education, and culture shape how individuals develop practical reasoning skills. For example, some environments prioritize problem-solving and critical thinking from an early age, while others may foster more passive learning, affecting how individuals reason about and act in the world.

6. Can Human Practical Reasoning Compete with AI?

Human-AI Collaboration:

Rather than viewing AI as a competitor, the future of practical reasoning may lie in human-AI collaboration. AI can assist with the heavy cognitive lifting—processing massive datasets, optimizing decisions based on algorithmic efficiency—while humans provide contextual awareness, moral reasoning, and emotional intelligence to guide decisions.

Expanding Human Capabilities:

There are possibilities for enhancing human practical reasoning abilities through neurotechnology. Technologies like brain-computer interfaces (BCIs) may one day augment the brain’s natural reasoning processes, enabling humans to process information more quickly, make more informed decisions, and act with greater precision.

The Limitations of AI:

AI is highly efficient in predictive tasks, data analysis, and rule-based reasoning, but it lacks the nuanced understanding that comes with human experience. Practical wisdom, as described by Aristotle, includes factors like virtue, empathy, and social responsibility—qualities that are difficult, if not impossible, to encode into AI systems. Hence, while AI may outperform humans in terms of speed and data processing, human intelligence remains more robust in complex, real-world decision-making.

7. Practical Reasoning and Ethics in AI Development

Ethical Action in AI Systems:

One of the most critical questions in AI practical reasoning is how to ensure that machines act ethically. Ethical frameworks for AI involve programming systems with guidelines for moral decision-making, but these are often based on rigid rules and lack the flexibility of human ethical judgment.
Bias and fairness: AI systems trained on biased datasets may make decisions that unintentionally harm certain groups, leading to ethical concerns about fairness and justice. Human oversight is crucial in ensuring that the actions of AI systems align with broader societal values.

These additional points provide a more nuanced understanding of practical reasoning, emphasizing its deep roots in human evolution, cognitive science, neuroscience, and AI development. They also raise important questions about the future of intelligence and action, particularly in the context of human-AI collaboration and the challenges of replicating the depth of human reasoning in machines.

Conclusion: The Role of Action in Intelligence and the Path Forward

The central role of action in intelligence cannot be overstated. From Aristotle’s concept of practical wisdom to McCarthy’s vision of AI with common sense, reasoning about actions has been a core part of what it means to be intelligent. While humans have evolved to be action-oriented, with practical reasoning shaped by our emotions, environment, and social contexts, AI is still in the early stages of replicating this ability in machines.

As we continue to develop more sophisticated AI systems, the goal will not only be to create machines that can reason like humans, but to understand how human intelligence and machine intelligence can work together, each bringing its unique strengths to bear on the challenges of the future.