The tools of molecular biology are used to solve an instance of the directed Hamiltonian path problem. A small graph is encoded in molecules of DNA and the `operations' of the computation are performed with standard protocols and enzymes. This experiment demonstrates the feasibility of carrying out computations at the molecular level.

We analyze the computational feasibility of checking temporal integrity constraints formulated in some sublanguages of first-order temporal logic. Our results illustrate the impact of the quantifier pattern on the complexity of this problem. The presence of a single quantifier in the scope of a temporal operator makes the problem undecidable. On the other hand, if no quantifiers are in the scope of a temporal operator and all the quantifiers are universal, temporal integrity checking can be done in exponential time. 1 Introduction As temporal databases become more widely used in practice [27, 28], the need arises to address database integrity issues that are specific to such databases. In particular, it is necessary to generalize the standard notion of static integrity (involving single database states) to temporal integrity (involving sequences of database states). This work is the first attempt to date to analyze the computational feasibility of checking temporal integrity constrain...

In [17], two of the authors have introduced techniques to build agents on top of arbitrary data structures, and to "agentize" new/existing programs. They provided a series of successively more sophisticated semantics for such agent systems, and showed that as these semantics become epistemically more desirable, a computational price may need to be paid. In this paper, we identify a class of agents that are called weakly regular---this is done by first identifying a fragment of agent programs [17] called weakly regular agent programs (WRAPs for short).

This paper surveys a wide range of proposed hypermachines, examining the resources that they require and the capabilities that they possess.

this paper contributes toward the goal of understanding how a computer can be programmed to learn by isolating features of incremental learning algorithms that theoretically enhance their learning potential. In particular, we examine the effects of imposing a limit on the amount of information that learning algorithm can hold in its memory as it attempts to This work was facilitated by an international agreement under NSF Grant 9119540.

Church thesis and its variants say roughly that all reasonable models of computation do not have more power than Turing Machines. In a contrapositive way, they say that any model with super-Turing power must have something unreasonable. Our aim is to discuss how much theoretical computer science can quantify this, by considering several classes of continuous time dynamical systems, and by studying how much they can be proved Turing or super-Turing. 1

When we only know the interval of possible values of a certain quantity (or a more general set of possible values), it is desirable to characterize this interval by supplying the user with the "simplest" element from this interval, and by characterizing how different from this value we can get. For example, if, for some unknown physical quantity x, measurements result in the interval [1:95; 2:1] of possible values, then, most probably, the physicist will publish this result as y 2. Similarly, a natural representation of the measurement result x 2 [3:141592; 3:141593] is x . In this paper, we show that the problem of choosing the simplest element from a given interval (or from a given set) is, in general, not algorithmically solvable. 1 1 In Case of Interval (or More General Set) Uncertainty, a User Would Like to Have a Representative Value from This Interval (Set) The value of a physical quantity y is usually obtained either by a direct measurement, or by an indirect mea...

Gold [Gol67] discovered a fundamental enumeration technique, the so-called identification-by-enumeration, a simple but powerful class of algorithms for learning from examples (inductive inference). We introduce a variety of more sophisticated (and more powerful) enumeration techniques and characterize their power. We conclude with the thesis that enumeration techniques are even universal in that each solvable learning problem in inductive inference can be solved by an adequate enumeration technique. This thesis is technically motivated and discussed. Keywords: Learning from examples, learning by search, identification by enumeration, enumeration techniques. Role of Search 1 1 Introduction The role of search, for learning from examples, is examined in a theoretical setting. Gold's seminal paper [Gol67] on inductive inference introduced a simple but powerful learning technique which became known as identificationby -enumeration. Identification-by-enumeration begins with an infi...

There is an obvious necessity to validate resp. verify complex systems. If human experts are involved in the implementation of any validation scenario, there arises the problem of the experts' competence. As a case study, the problem of expertise for systems validation is investigated in the area of learning systems validation. It turns out that certain human expertise sufficient to accomplish certain validation tasks is substantially non-recursive. Consequently, there is no way to replace humans by computer programs for those validation tasks. Validation of Complex Systems -- Necessity, Problems, and Solutions There is an obvious necessity to validate resp. verify complex systems. It might easily happen that :::the inability to adequately evaluate systems may become the limiting factor in our ability to employ systems that our technology and knowledge will allow us to design. (cf. (Wise & Wise 1993)) Unfortunately, there are numerous severe accidents bearing abundant evidence for th...

Previous work in inductive inference dealt mostly with finding one or several machines (IIMs) that successfully learn a collection of functions. Herein we start with a class of functions and consider the learner set of all IIMs that are successful at learning the given class. Applying this perspective to the case of team inference leads to the notion of diversification for a class of functions. This enables us to distinguish between several flavors of IIMs all of which must be represented in a team learning the given class. 2 1 Introduction All current theoretical approaches to machine learning tend to focus on a particular machine or a collection of machines and then find the class of concepts which can be learned by these machines under certain constraints defining a criterion of successful learning [AS83, OSW86]. In this paper we investigate the dual problem: Given some set of concepts, which algorithms can learn all those concepts? From [AGS89] we know that in the theory ...