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Alternating refinement relations
 In Proceedings of the Ninth International Conference on Concurrency Theory (CONCUR’98), volume 1466 of LNCS
, 1998
"... Abstract. Alternating transition systems are a general model for composite systems which allow the study of collaborative as well as adversarial relationships between individual system components. Unlike in labeled transition systems, where each transition corresponds to a possible step of the syste ..."
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Cited by 164 (20 self)
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Abstract. Alternating transition systems are a general model for composite systems which allow the study of collaborative as well as adversarial relationships between individual system components. Unlike in labeled transition systems, where each transition corresponds to a possible step of the system (which may involve some or all components), in alternating transition systems, each transition corresponds to a possible move in a game between the components. In this paper, we study refinement relations between alternating transition systems, such as “Does the implementation refine the set £ of specification components without constraining the components not in £? ” In particular, we generalize the definitions of the simulation and trace containment preorders from labeled transition systems to alternating transition systems. The generalizations are called alternating simulation and alternating trace containment. Unlike existing refinement relations, they allow the refinement of individual components within the context of a composite system description. We show that, like ordinary simulation, alternating simulation can be checked in polynomial time using a fixpoint computation algorithm. While ordinary trace containment is PSPACEcomplete, we establish alternating trace containment to be EXPTIMEcomplete. Finally, we present logical characterizations for the two preorders in terms of ATL, a temporal logic capable of referring to games between system components. 1
Model Checking vs. Theorem Proving: A Manifesto
, 1991
"... We argue that rather than representing an agent's knowledge as a collection of formulas, and then doing theorem proving to see if a given formula follows from an agent's knowledge base, it may be more useful to represent this knowledge by a semantic model, and then do model checking to se ..."
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Cited by 137 (6 self)
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We argue that rather than representing an agent's knowledge as a collection of formulas, and then doing theorem proving to see if a given formula follows from an agent's knowledge base, it may be more useful to represent this knowledge by a semantic model, and then do model checking to see if the given formula is true in that model. We discuss how to construct a model that represents an agent's knowledge in a number of different contexts, and then consider how to approach the modelchecking problem.
Knowledge, probability, and adversaries
 Journal of the ACM
, 1993
"... Abstract: What should it mean for an agent toknowor believe an assertion is true with probability:99? Di erent papers [FH88, FZ88a, HMT88] givedi erent answers, choosing to use quite di erent probability spaces when computing the probability that an agent assigns to an event. We showthat each choice ..."
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Cited by 84 (23 self)
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Abstract: What should it mean for an agent toknowor believe an assertion is true with probability:99? Di erent papers [FH88, FZ88a, HMT88] givedi erent answers, choosing to use quite di erent probability spaces when computing the probability that an agent assigns to an event. We showthat each choice can be understood in terms of a betting game. This betting game itself can be understood in terms of three types of adversaries in uencing three di erent aspects of the game. The rst selects the outcome of all nondeterministic choices in the system� the second represents the knowledge of the agent's opponent in the betting game (this is the key place the papers mentioned above di er) � the third is needed in asynchronous systems to choose the time the bet is placed. We illustrate the need for considering all three types of adversaries with a number of examples. Given a class of adversaries, we show howto assign probability spaces to agents in a way most appropriate for that class, where \most appropriate " is made precise in terms of this betting game. We conclude by showing how di erent assignments of probability spaces (corresponding to di erent opponents) yield di erent levels of guarantees in probabilistic coordinated attack.
Reasoning about Noisy Sensors and Effectors in the Situation Calculus
 Artificial Intelligence
, 1998
"... 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 t ..."
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Cited by 82 (5 self)
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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 errorprone 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 ...
Updating Probabilities
, 2002
"... As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a "naive space", which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR ("coarsening a ..."
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Cited by 69 (4 self)
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As examples such as the Monty Hall puzzle show, applying conditioning to update a probability distribution on a "naive space", which does not take into account the protocol used, can often lead to counterintuitive results. Here we examine why. A criterion known as CAR ("coarsening at random") in the statistical literature characterizes when "naive" conditioning in a naive space works. We show that the CAR condition holds rather infrequently, and we provide a procedural characterization of it, by giving a randomized algorithm that generates all and only distributions for which CAR holds. This substantially extends previous characterizations of CAR. We also consider more generalized notions of update such as Jeffrey conditioning and minimizing relative entropy (MRE). We give a generalization of the CAR condition that characterizes when Jeffrey conditioning leads to appropriate answers, and show that there exist some very simple settings in which MRE essentially never gives the right results. This generalizes and interconnects previous results obtained in the literature on CAR and MRE.
Algorithmic Knowledge
 Proc. Second Conference on Theoretical Aspects of Reasoning about Knowledge
, 1994
"... : The standard model of knowledge in multiagent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this turns out not to be a problem. Knowledge is viewed as be ..."
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Cited by 63 (10 self)
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: The standard model of knowledge in multiagent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this turns out not to be a problem. Knowledge is viewed as being ascribed by the system designer to the agents; agents are not assumed to compute their knowledge in any way, nor is it assumed that they can necessarily answer questions based on their knowledge. Nevertheless, in many applications that we are interested in, agents need to act on their knowledge. In such applications, an externally ascribed notion of knowledge is insufficient: clearly an agent can base his actions only on what he explicitly knows. Furthermore, an agent that has to act on his knowledge has to be able to compute this knowledge; we do need to take into account the algorithms available to the agent, as well as the "effort" required to compute knowledge. In this paper, we show...
What Can Machines Know? On the Properties of Knowledge in Distributed Systems
 Journal of the ACM
, 1996
"... It has been argued that knowledge is a useful tool for designing and analyzing complex systems. The notion of knowledge that seems most relevant in this context is an external, informationbased notion that can be shown to satisfy all the axioms of the modal logic S5. We carefully examine the pro ..."
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Cited by 58 (11 self)
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It has been argued that knowledge is a useful tool for designing and analyzing complex systems. The notion of knowledge that seems most relevant in this context is an external, informationbased notion that can be shown to satisfy all the axioms of the modal logic S5. We carefully examine the properties of this notion of knowledge and show that they depend crucially, and in subtle ways, on assumptions we make about the system and about the language used for describing knowledge. We present a formal model in which we can capture various assumptions frequently made about systems, such as whether they are deterministic or nondeterministic, whether knowledge is cumulative (which means that processes never "forget"), and whether or not the "environment" affects the state transitions of the processes. We then show that under some assumptions about the system and the language, certain states of knowledge are not attainable and the axioms of S5 do not completely characterize the pr...
KnowledgeBased Programs
, 1996
"... Reasoning about activities in a distributed computer system at the level of the knowledge of individuals and groups allows us to abstract away from many concrete details of the system we are considering. In this paper, we make use of two notions introduced in our recent book to facilitate designing ..."
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Cited by 57 (12 self)
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Reasoning about activities in a distributed computer system at the level of the knowledge of individuals and groups allows us to abstract away from many concrete details of the system we are considering. In this paper, we make use of two notions introduced in our recent book to facilitate designing and reasoning about systems in terms of knowledge. The first notion is that of a knowledgebased program. A knowledgebased program is a syntactic object: a program with tests for knowledge. The second notion is that of a context, which captures the setting in which a program is to be executed. In a given context, a standard program (one without tests for knowledge) is represented by (i.e., corresponds in a precise sense to) a unique system. A knowledgebased program, on the other hand, may be represented by no system, one system, or many systems. In this paper, we provide a sufficient condition for a knowledgebased program to be represented in a unique way in a given context. This condit...