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Assumptions of Decision-Making Models in AGI

This paper discusses the limitations of traditional decision-making models in the context of Artificial General Intelligence (AGI) and introduces NARS (Non-Axiomatic Reasoning System) as a normative model for decision-making that does not rely on these assumptions. The paper explores the assumptions related to task, belief, desire, and budget and highlights how NARS, with its adaptive and resource-efficient approach, addresses these challenges.

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Assumptions of Decision-Making Models in AGI

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  1. Assumptions of Decision-Making Models in AGI Pei Wang Temple University, USA Patrick Hammer Graz University of Technology, Austria

  2. Traditional Decision-Making models (DM) (reinforcement learning, markov decision processes, game theory, etc.) assume: On task: Selection of the best action from a list of candidates for each state. On belief: Consequent states are given by a probability distribution for each action. On desire: The system’s desires are given by a value function defined on states. On budget: Enough existing resources to use the selection algorithm. Traditional Assumptions

  3. In case of AGI, the four assumptions don’t hold: On task: Deciding which candidates to consider is a crucial part itself. On belief: Neither preconditions nor consequences of an action are fully specified. On desire: How much a situation is desired is not directly measured by an existing function. On budget: The system cannot afford the resources to consider all possibilities. The Situation in AGI

  4. NARS Overview • NARS (Non-Axiomatic Reasoning System) is an AGI system designed to be adaptive and to work with insufficient knowledge and resources. • It uniformly carries out many cognitive functions, such as reasoning, learning, decision-making, etc., using a single technique • The decision making process in NARS does not depend on any of the previous assumptions

  5. Truth-value in NARS • Under the assumption of insufficient knowledge and resources (AIKR), truth of a belief can only be judged according to parts of past experience • Amount of positive and negative evidence: w+ and w-. With w = w+ + w-, the truth-value of the belief is <f,c>, where f = w+/ w, c = w / (w+1) • The truth-values can be revised by new evidence.

  6. Task in NARS At any moment, NARS works on multiple tasks: • A judgment to be absorbed into beliefs • A question to be answered according to beliefs • A goal to be achieved by executing operations, guided by beliefs The desire-value of a goal G is the truth-value of G ═> D, where D is a virtual statement for a “desired situation”

  7. Operation in NARS • An operation is what the system can execute to change its external/internal states • The most important information about an operation are its pre- and post-conditions, represented as statements. • A typical belief on an operation takes the form condition ⇒ (operation ⇒ consequence)

  8. Decision Making in NARS • In a narrow sense, in NARS “decision making” means the decision on whether to pursue a goal, including direct execution in case that it’s an operation. • This decision is mainly based on the desire-value of the statement under consideration. • In a broad sense, all reasoning activities in the system contribute to the decisions.

  9. The Assumption on Task • In NARS, the task of “decision making” is not to select one from a given list of actions, but to decide whether to pursue a goal. • In every situation, the applicable actions are usually not explicitly listed, but need to be found or composed by the system itself. • An important aspect of decision making is goal derivation via backward inference.

  10. The Assumption on Belief • In NARS, the preconditions and consequences of an operation are expressed as “statements”, rather than “states”, so they can be incomplete. • The truth-value in NARS is not probability, because it is based on partial past experience, rather than the objective world. • The system's beliefs can be inconsistent: Different sections of experience may lead to different opinions.

  11. The Assumption on Desire • In NARS, the desire-value of a statement summarizes its relation with the considered other goals. • Since goals come and go in real time, the desire-values cannot be obtained from a function that maps states to utility or reward, but must be derived at runtime. • The behaviors of an AGI will not be determined by a single “supergoal”, but by many (given or derived) goals that compete with each other.

  12. The Assumption on Budget • “Optimum decisions made with sufficient resource” and “optimum decisions made with insufficient resource” are different problems that demand different solutions. • NARS works in realtime, which means to handle each problem instance according to its demand on response time, and case by case, rather than depending on a “decision-making algorithm” with a fixed resource cost.

  13. Conclusion • The fundamental assumptions of the traditional decision-making models cannot be satisfied in the context of AGI. • NARS is a normative model for decision making that does not make any of the assumptions • An AGI system should assume insufficient knowledge and resources, with respect to the problems it wants to solve

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