What is action selection?

by Stephen M. Walker II, Co-Founder / CEO

What is action selection?

Action selection in artificial intelligence (AI) refers to the process by which an AI agent determines what to do next. It's a fundamental mechanism for integrating the design of intelligent systems and is a key aspect of AI development.

The action selection process involves choosing an action from a set of alternatives based on the agent's current state and the environmental context. This process is crucial for AI agents to function effectively in dynamic and unpredictable environments, and it often needs to be performed in real time.

The level of abstraction used to specify an 'act' can vary greatly. At the most basic level, an act could be anything from a simple movement to a complex sequence of actions. The set of possible actions is typically predefined and fixed for an artificial action-selection mechanism, but in nature, agents can control action at various levels.

The action selection mechanism (ASM) not only determines the agent's actions but also directs its perceptual attention and updates its memory. Ideally, the ASM should be able to learn and adapt, but there are challenges related to combinatorial complexity and computational tractability.

Theories of action selection range from dynamic models, where there are no discrete acts being selected but only continuous integrated processes, to logic-based strictly-sequential provably-optimal lists of actions referred to as plans.

Characteristics of the action selection problem

The action selection problem is a fundamental issue in artificial intelligence and computational cognitive science, associated with intelligent agents and animats—artificial systems that exhibit complex behavior in an agent environment. It characterizes the most basic problem of intelligent systems: deciding what to do next.

Key characteristics of the action selection problem include:

  1. Complexity — The main challenge for action selection is the complexity of the problem. This complexity arises from the need to determine the level of abstraction used for understanding action selection, the environment the agents operate in, and the behavior of the agents themselves.

  2. Multiple Possible Actions — There are numerous possible actions that an AI system can take, such as developing better algorithms for learning and decision-making, creating new ways to evaluate and compare AI systems, and investigating the ethical and social implications of AI.

  3. Action Selection Mechanism (ASM) — The ASM is a mechanism that determines the actions of the agents. This mechanism may be highly distributed and is crucial for building an intelligent agent.

  4. Conflicting and Heterogeneous Goals — Action selection involves the higher-level problem of choice between conflicting and heterogeneous goals. These goals are pursued in parallel and may sometimes combine to achieve larger-scale goals, but in general, they simply interfere with each other.

  5. Neural Basis — The neural basis of action selection involves structures like the caudate nucleus and posterior parietal cortex, which establish the distinct causal relationships from which specific action-outcome associations are formed.

  6. Learning and Adaptation — Action selection involves learning and adaptation. The ASM causes modifications in the agent's behavior when learning new things and helps it adapt to new situations.

  7. Emergent Property — One fundamental question about action selection is whether it is really a problem at all for an agent or whether it is just a description of an emergent property of an intelligent agent's behavior.

  8. Optimal Actions — Research on action selection includes work on the problem of determining optimal actions, which may involve learning.

  9. Action-Outcome Learning — From an associative perspective, the acquisition of new goal-directed actions requires the encoding of specific action-outcome (AO) relationships.

  10. Dynamic Action Selection — In reinforcement learning, dynamic action selection allows the agent to adjust the value of an action by a variable amount, rather than a fixed one.

These characteristics make the action selection problem a central issue in the simulation of whole creatures and a focus of research in various fields such as ethology, psychology, neurobiology, computational neuroscience, artificial intelligence, and robotics.

Action Selection Mechanisms

Action selection mechanisms (ASMs) are the systems that enable an agent to choose an action from a range of options. These mechanisms are crucial for the decision-making process in AI systems, driving efficiency, adaptability, and optimal performance.

There are various methods for action selection, each with its own strengths and weaknesses. Some of these methods include:

  1. Random Selection — This method involves randomly choosing an action from the available options. It ensures exploration and helps avoid biases that may exist within the system. However, it lacks reasoning and might not lead to the desired outcomes.

  2. Greedy Selection — In contrast to random selection, a greedy action-selection method focuses on maximizing immediate gains. Here, the AI system chooses the action that is expected to provide the greatest immediate benefit.

  3. Softmax Action Selection — This method varies the action probabilities as a graded function of estimated value. The greedy action is still given the highest selection probability, but all the others are ranked and weighted according to their value estimates.

  4. Reinforcement Learning — This approach uses a reward system to guide the agent towards actions that lead to positive outcomes. It's argued that reinforcement learning has been overlooked in the solution of the action selection problem.

The choice of action selection method depends on the specific requirements of the AI system and the environment in which it operates. For instance, random selection might be suitable for the initial stages of AI training, while greedy selection could be more appropriate for scenarios that demand precise actions.

What are some common methods for action selection?

Common methods for action selection in AI include:

  1. Symbol-based systems or classical planning — These systems use symbolic representations of problems and logic to plan out actions step by step.

  2. Dynamic planning techniques — These are popular in real-time systems and involve planning that can adapt to changes in the environment.

  3. Reinforcement learning — This method uses rewards and penalties to guide the selection of actions, balancing exploration of new actions with exploitation of known good actions. A specific example is the Cuckoo Search Algorithm-based action-selection method, which balances exploration and exploitation without requiring extensive parameter tuning.

  4. Random selection — In some cases, actions are selected randomly, which can be useful in environments with insufficient data.

  5. Neural networks — These can be trained to select actions based on pattern recognition from input data.

  6. Evolutionary algorithms — These simulate the process of natural selection to evolve better action-selection strategies over time.

  7. Hybrid approaches — Combining multiple methods, such as neural networks with evolutionary algorithms, to leverage the strengths of each.

Each method has its own advantages and is suitable for different scenarios depending on the complexity of the task, the dynamics of the environment, and the computational resources available.

Theories of action selection in nature

Action selection is the process of deciding what to do next, a problem faced by all autonomous entities, including animals and artificial agents. It involves two parts: defining what constitutes an action and determining how actions are selected.

Several theories have been proposed to explain action selection in nature:

  1. Innate Releasing Mechanism — This theory, proposed by Konrad Lorenz and Nikolaas Tinbergen, suggests that instinctive behaviors (fixed action patterns) are triggered by specific stimuli.

  2. Affordance Competition Hypothesis — Proposed by Cisek, this theory suggests that action selection is a competition between different potential actions, or "affordances", that are available in the environment.

  3. Optimal Strategy Theory — This theory suggests that animals make decisions to maximize their chances of survival and reproductive success. The optimal behavior can be mathematically formulated and compared with the actual behavior of the animal.

  4. Heuristic Approach — This approach suggests that from a set of incompatible competitors, only one action should be allowed expression at a given time. The selection of a single winner is often based on a heuristic, or rule of thumb.

  5. Preference Methods — This approach identifies factors that contribute to action choices, especially for object-manipulation tasks.

  6. Animal Perspective Approach — This approach suggests that decisions made by the agent can be analyzed from its point of view, accounting for its sensory and motor capabilities, environmental conditions, and affordances.

  7. Agent-Based Models — These models allow us to test the coherence of proposed social and biological theories. They can generate data about theories and deliver new insights in many domains.

  8. Evolutionary Psychology — This approach suggests that human behaviors are not a direct product of natural selection but rather the product of psychological mechanisms that were selected for.

These theories and models provide a comprehensive understanding of how action selection works in nature, from instinctive behaviors to complex decision-making processes. They offer insights into the mechanisms that underlie action selection, helping us understand not only animal behavior but also the behavior of artificial agents.

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