Data sharing is harder to reward

Data Integration Is Harder than You Thought

High Reward Makes Items Easier to Remember, but Harder to Bind to a New Temporal Context

Learning through reward is central to adaptive behavior. Indeed, items are remembered better if they are experienced while participants expect a reward, and people can deliberately prioritize memory for high- over low-valued items. Do memory advantages for high-valued items only emerge after deliberate prioritization in encoding? Or, do reward-based memory enhancements also apply to unrewarded ...

What Makes Petri Nets Harder to Verify: Stack or Data?

We show how the yardstick construction of Stockmeyer, also developed as counter bootstrapping by Lipton, can be adapted and extended to obtain new lower bounds for the coverability problem for two prominent classes of systems based on Petri nets: Ackermann-hardness for unordered data Petri nets, and Tower-hardness for pushdown vector addition systems.

Is it Harder to Parse Chinese, or the Chinese Treebank?

Is Belief Revision Harder Than You Thought?

Suppose one wishes to construct, use, and maintain a knowledge base (KB) of beliefs about the real world, even though the facts about that world are only partially known. In the AI domain, this problem arises when an agent has a base set of beliefs that reflect partial knowledge about the world, and then tries to incorporate new, possibly contradictory knowledge into this set of beliefs. We cho...