CoRR: A Computing Research Repository
Discusses how CoRR was set up and some policy issues involved with setting up such a repository.
arXiv subjects
Publications and source records attributed to Joseph Y. Halpern.
Discusses how CoRR was set up and some policy issues involved with setting up such a repository.
Knowledge-based program are programs with explicit tests for knowledge. They have been used successfully in a number of applications. Sanders has pointed out what seem to be a counterintuitive property of knowledge-based programs. Roughly speaking, they do not satisfy a certain monotonicity property, while standard programs (ones without tests for knowledge) do. It is shown that there are two ways of defining the monotonicity property, which agree for standard programs. Knowledge-based programs satisfy the first, but do not satisfy the second. It is further argued by example that the fact that they do not satisfy the second is actually a feature, not a problem. Moreover, once we allow the more general class of knowledge-based specifications, standard programs do not satisfy the monotonicity property either.
Abadi has introduced a logic to explicate the meaning of local names in SDSI, the Simple Distributed Security Infrastructure proposed by Rivest and Lampson. Abadi's logic does not correspond precisely to SDSI, however; it draws conclusions about local names that do not follow from SDSI's name resolution algorithm. Moreover, its semantics is somewhat unintuitive. This paper presents the Logic of Local Name Containment, which does not suffer from these deficiencies. It has a clear semantics and provides a tight characterization of SDSI name resolution. The semantics is shown to be closely related to that of logic programs, leading to an approach to the efficient implementation of queries concerning local names. A complete axiomatization of the logic is also provided.
Levesque introduced a notion of ``only knowing'', with the goal of capturing certain types of nonmonotonic reasoning. Levesque's logic dealt with only the case of a single agent. Recently, both Halpern and Lakemeyer independently attempted to extend Levesque's logic to the multi-agent case. Although there are a number of similarities in their approaches, there are some significant differences. In this paper, we reexamine the notion of only knowing, going back to first principles. In the process, we simplify Levesque's completeness proof, and point out some problems with the earlier definitions. This leads us to reconsider what the properties of only knowing ought to be. We provide an axiom system that captures our desiderata, and show that it has a semantics that corresponds to it. The axiom system has an added feature of interest: it includes a modal operator for satisfiability, and thus provides a complete axiomatization for satisfiability in the logic K45.
The assumptions needed to prove Cox's Theorem are discussed and examined. Various sets of assumptions under which a Cox-style theorem can be proved are provided, although all are rather strong and, arguably, not natural.
Complete axiomatizations and exponential-time decision procedures are provided for reasoning about knowledge and common knowledge when there are infinitely many agents. The results show that reasoning about knowledge and common knowledge with infinitely many agents is no harder than when there are finitely many agents, provided that we can check the cardinality of certain set differences G - G', where G and G' are sets of agents. Since our complexity results are independent of the cardinality of the sets G involved, they represent improvements over the previous results even with the sets of agents involved are finite. Moreover, our results make clear the extent to which issues of complexity and completeness depend on how the sets of agents involved are represented.
We argue that the tools of decision theory need to be taken more seriously in the specification and analysis of systems. We illustrate this by considering a simple problem involving reliable communication, showing how considerations of utility and probability can be used to decide when it is worth sending heartbeat messages and, if they are sent, how often they should be sent.
We identify two unreasonable, though standard, assumptions made by database query optimizers that can adversely affect the quality of the chosen evaluation plans. One assumption is that it is enough to optimize for the expected case---that is, the case where various parameters (like available memory) take on their expected value. The other assumption is that the parameters are constant throughout the execution of the query. We present an algorithm based on the ``System R''-style query optimization algorithm that does not rely on these assumptions. The algorithm we present chooses the plan of the least expected cost instead of the plan of least cost given some fixed value of the parameters. In execution environments that exhibit a high degree of variability, our techniques should result in better performance.
We describe the Computing Research Repository (CoRR), a new electronic archive for rapid dissemination and archiving of computer science research results. CoRR was initiated in September 1998 through the cooperation of ACM, LANL (Los Alamos National Laboratory) e-Print archive, and NCSTRL (Networked Computer Science Technical Research Library. Through its implementation of the Dienst protocol, CoRR combines the open and extensible architecture of NCSTRL with the reliable access and well-established management practices of the LANL XXX e-Print repository. This architecture will allow integration with other e-Print archives and provides a foundation for a future broad-based scholarly digital library. We describe the decisions that were made in creating CoRR, the architecture of the CoRR/NCSTRL interoperation, and issues that have arisen during the operation of CoRR.
Agents interacting with an incompletely known world need to be able to reason about the effects of their actions, and to gain further information about that world they need to use sensors of some sort. Unfortunately, both the effects of actions and the information returned from sensors are subject to error. To cope with such uncertainties, the agent can maintain probabilistic beliefs about the state of the world. With probabilistic beliefs the agent will be able to quantify the likelihood of the various outcomes of its actions and is better able to utilize the information gathered from its error-prone actions and sensors. In this paper, we present a model in which we can reason about an agent's probabilistic degrees of belief and the manner in which these beliefs change as various actions are executed. We build on a general logical theory of action developed by Reiter and others, formalized in the situation calculus. We propose a simple axiomatization that captures an agent's state of belief and the manner in which these beliefs change when actions are executed. Our model displays a number of intuitively reasonable properties.
We introduce a new approach to modeling uncertainty based on plausibility measures. This approach is easily seen to generalize other approaches to modeling uncertainty, such as probability measures, belief functions, and possibility measures. We focus on one application of plausibility measures in this paper: default reasoning. In recent years, a number of different semantics for defaults have been proposed, such as preferential structures, $ε$-semantics, possibilistic structures, and $κ$-rankings, that have been shown to be characterized by the same set of axioms, known as the KLM properties. While this was viewed as a surprise, we show here that it is almost inevitable. In the framework of plausibility measures, we can give a necessary condition for the KLM axioms to be sound, and an additional condition necessary and sufficient to ensure that the KLM axioms are complete. This additional condition is so weak that it is almost always met whenever the axioms are sound. In particular, it is easily seen to hold for all the proposals made in the literature.
Conditional logics play an important role in recent attempts to formulate theories of default reasoning. This paper investigates first-order conditional logic. We show that, as for first-order probabilistic logic, it is important not to confound statistical conditionals over the domain (such as ``most birds fly''), and subjective conditionals over possible worlds (such as ``I believe that Tweety is unlikely to fly''). We then address the issue of ascribing semantics to first-order conditional logic. As in the propositional case, there are many possible semantics. To study the problem in a coherent way, we use plausibility structures. These provide us with a general framework in which many of the standard approaches can be embedded. We show that while these standard approaches are all the same at the propositional level, they are significantly different in the context of a first-order language. Furthermore, we show that plausibilities provide the most natural extension of conditional logic to the first-order case: We provide a sound and complete axiomatization that contains only the KLM properties and standard axioms of first-order modal logic. We show that most of the other approaches have additional properties, which result in an inappropriate treatment of an infinitary version of the lottery paradox.