Knowledge = Observation + Memory + Computation
نویسندگان
چکیده
We compare three notions of knowledge in concurrent system: memoryless knowledge, knowledge of perfect recall, and causal knowledge. Memoryless knowledge is based only on the current state of a process, knowledge of perfect recall can take into account the local history of a process, and causal knowledge depends on the causal past of a process, which comprises the information a process can obtain when all processes exchange the information they have when performing joint transitions. We compare these notions in terms of knowledge strength, number of bits required to store this information, and the complexity of checking if a given process has a given knowledge. We show that all three notions of knowledge can be implemented using finite memory. Causal knowledge proves to be strictly more powerful than knowledge with perfect recall, which in turn proves to be strictly more powerful than memoryless knowledge. We show that keeping track of causal knowledge is cheaper than keeping track of knowledge of perfect recall.
منابع مشابه
Entropy Semiring Forward-backward Algorithm for HMM Entropy Computation
The paper presents Entropy Semiring Forwardbackward algorithm (ESRFB) and its application for memory efficient computation of the subsequence constrained entropy and state sequence entropy of a Hidden Markov Model (HMM) when an observation sequence is given. ESRFB is based on forwardbackward recursion over the entropy semiring, having the lower memory requirement than the algorithm developed by...
متن کامل1 Design and Analysis Tools for Concurrent Blackboard Systems
The blackboard is a centralized global data structure, often partitioned in a hierarchical manner, used to represent the problem domain. The blackboard is also used to allow inter-knowledge source communication and acts as a shared memory visible to all of the knowledge sources. A knowledge source is a highly specialized, highly independent process that takes inputs from the blackboard data str...
متن کاملParallel Reduction of Matrices in Gröbner Bases Computations
Unfortunately the computation is time-and memory intensive. Mathematical knowledge is used to optimize the algorithms. Computer science provides another possibility to increase the computations: parallelization 2 / 24 Motivation Gröbner bases are used, to solve polynomial equation systems, move robotics, verify programs,. .. Unfortunately the computation is time-and memory intensive. Mathematic...
متن کاملAn Architecture for Hybrid Creative Reasoning
Creativity is one of the most remarkable characteristics of the human mind. It is thus natural that Artificial Intelligence’s research groups have been working towards the study and proposal of adequate computational models to creativity. Artificial creative systems are potentially effective in a wide range of artistic, architectural and engineering domains where detailed problem specification ...
متن کاملDoes Functional Knowledge Have a Privileged Status in the Speeded Computation of Word Meaning?
Theories of semantic memory differ in claims about the relative importance of knowledge types (e.g., sensory versus functional) in object concepts, giving rise to varying predictions about the rate at which they become available during word meaning computation. We report evidence that sensory features are computed faster than functional features, and interpret these results in terms of perceptu...
متن کامل