We investigate the complexity of the classical problem of optimal policy computation in Markov decision processes. All three variants of the problem finite horizon, infinite horizon discounted, and infinite horizon average cost were known to be solvable in polynomial time by dynamic programming finite horizon problems, linear programming, or successive approximation techniques infinite horizon. We show that they are complete for P, and therefore most likely cannot be solved by highly parallel algorithms. We also show that, in contrast, the deterministic cases of all three problems can be solved very fast in parallel. The version with partially observed states is shown to be PSPACE-complete, and thus even less likely to be solved in polynomial time than the NP-complete problems; in fact, we show that, most likely, it is not possible to have an efficient on-line implementation involving polynomial time on-line computations and memory of an optimal policy, even if an arbitrary amount of precomputation is allowed. Finally, the variant of the problem in which there are no observations is shown to be NP-complete.

/pdf/the-complexity-of-markov-decision-processes-51wd5q31f7.pdf

The Complexity of Markov Decision Processes

Within the context of an algebraic theory of processes, an equational specification of process cooperation is provided. Four cases are considered: free merge or interleaving, merging with communication, merging with mutual exclusion of tight regions, and synchronous process cooperation. The rewrite system behind the communication algebra is shown to be confluent and terminating (modulo its permutative reductions). Further, some relationships are shown to hold between the four concepts of merging. © 1984 Academic Press, Inc.

/pdf/process-algebra-for-synchronous-communication-3g5i7om23k.pdf

Process algebra for synchronous communication

Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art.

Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey

http://compalg.inf.elte.hu/~tony/Kutatas/BinPacking/Johnson-BinPacking-NP-Column-JAlg-2002.pdf

The NP-completeness column: An ongoing guide

The class NC consists of problems solvable very fast (in time polynomial in log n ) in parallel with a feasible (polynomial) number of processors. Many natural problems in NC are known; in this paper an attempt is made to identify important subclasses of NC and give interesting examples in each subclass. The notion of NC 1 -reducibility is introduced and used throughout (problem R is NC 1 -reducible to problem S if R can be solved with uniform log-depth circuits using oracles for S ). Problems complete with respect to this reducibility are given for many of the subclasses of NC . A general technique, the “parallel greedy algorithm,” is identified and used to show that finding a minimum spanning forest of a graph is reducible to the graph accessibility problem and hence is in NC 2 (solvable by uniform Boolean circuits of depth O (log 2 n ) and polynomial size). The class LOGCFL is given a new characterization in terms of circuit families. The class DET of problems reducible to integer determinants is defined and many examples given. A new problem complete for deterministic polynomial time is given, namely, finding the lexicographically first maximal clique in a graph. This paper is a revised version of S. A. Cook, (1983, in “Proceedings 1983 Intl. Found. Comut. Sci. Conf.,” Lecture Notes in Computer Science Vol. 158, pp. 78–93, Springer-Verlag, Berlin/New York).

A taxonomy of problems with fast parallel algorithms

On optimal trees

Algorithms and Computations

Traditionally, mathematical logic has been content with 'in principle' formalizations of deductive inference, which place little emphasis on the description of practical reasoning. Requirements for formal descriptions of practical inference methods are now emerging however. For example, interactive reasoning systems are needed for verification of computer systems, and should support practically convenient reasoning techniques as strongly as possible. This paper describes a proof paradigm which formalises a hierarchical, problem-reduction style of reasoning which is widely useful in practical reasoning. It is a goal directed paradigm, which gives a central role to equivalence transformations. A hierarchy of subgoals can co-exist at a single point in the proof, and these subgoals may be of arbitrary type. The approach allows good access to contextual information when transforming subgoals, and can be applied to a variety of logics.

Formalizing a Hierarchical Structure of Practical Mathematical Reasoning

Qu-Prolog: an extended Prolog for meta level programming

A Graph-Like Lambda Calculus for Which Leftmost-Overmost Reduction is Optimal

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