Linearizable Read/Write Objects

We study the cost of implementing linearizable read/write objects for shared-memory multiprocessors under various assumptions on the available timing information. We take as cost measure the worst-case response time of performing an operation in distributed implementations of virtual shared memory consisting of such objects. It is assumed that processes have clocks that run at the same rate as real time and all messages incur a delay in the range [d \Gamma u; d] for some known constants u and d, 0 u d. In the perfect clocks model, where processes have perfectly synchronized clocks and every message incurs a delay of exactly d, we present a family of optimal linearizable implementations, parameterized by a constant fi, 0 fi 1, for which the worst-case response times for read and write operations are fid and (1 \Gamma fi)d, respectively. The parameter fi may be appropriately chosen to account for the relative frequencies of read and write operations. Our main result is the first kno..

Mathematical linguistics and automatic translation; report to the National Science Foundation.

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Learning to Reason

We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here views learning as an integral part of the inference process, and suggests that learning and reasoning should be studied together. The Learning to Reason framework combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework the intelligent agent is given access to its favorite learning interface, and is also given a grace period in which it can interact with this interface and construct a representation KB of the world W . The reasoning performance is measured only after this period, when the agent is presented with queries ff from some query language, relevant to the world, and has to answer whether W implies ff. The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the "world". Since th..

Learning to Reason

We introduce a new framework for the study of reasoning. The Learning (in order) to Reason approach developed here combines the interfaces to the world used by known learning models with the reasoning task and a performance criterion suitable for it. In this framework the intelligent agent is given access to her favorite learning interface, and is also given a grace period in which she can interact with this interface and construct her representation KB of the world W . Her reasoning performance is measured only after this period, when she is presented with queries ff from some query language, relevant to the world, and has to answer whether W implies ff. The approach is meant to overcome the main computational difficulties in the traditional treatment of reasoning which stem from its separation from the "world". First, by allowing the reasoning task to interface the world (as in the known learning models), we avoid the rigid syntactic restriction on the intermediate knowledge repres..