arXiv:2606.24672v1 Announce Type: new Abstract: In many decision-making scenarios, acquiring information incurs different costs. We consider the problem of constructing a deterministic evaluation strategy that minimizes the expected cost of evaluating a propositional formula under variable costs and a probability distribution over truth assignments. We present a branch-and-bound algorithm with variable-selection heuristics, pruning, and caching. To the best of our knowledge, it is the first practical exact algorithm for this level of generality. Experiments on random instances demonstrate scal
The continuous drive for more efficient AI and decision-making systems pushes for better algorithms to manage computational costs, especially with increasing model complexity.
This development offers a more practical and general method for cost-optimal decision-making, which is crucial for resource-constrained AI applications and automated systems.
The ability to minimize expected evaluation costs for propositional formulas under variable costs and probability distributions provides a new foundational tool for AI and automated reasoning.
- · AI algorithm developers
- · Automation industries
- · Resource-constrained computing
- · Logistics and operational research
- · Inefficient decision-making systems
- · High-cost inference pipelines
Improved efficiency and cost-effectiveness in AI decision-making processes.
Faster development and deployment of complex AI agents that operate under real-world economic constraints.
Enhanced operational autonomy and reduced human intervention in systems where information acquisition has varied costs.
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Read at arXiv cs.AI