a new approach to multipath correction of constant modulus signals an adaptive digital filtering algorithm that can compensate for both frequency selective multipath and interference on constant envelope modulated signals is presented the method exploits the fact that multipath reception and various interference sources generate incidental amplitude modulation on the received signal a class of so called constant modulus performance functions is developed which sense this am term but are insensitive to the angle modulation simple adaptive algorithms for finite impulse response fir digital filters are developed which employ a gradient search of the performance function one of the resulting algorithms is simulated for the example of an fm signal degraded by specular multipath propagation substantial improvements in noise power ratio npr are observed e g 25 db with moderately rapid convergence time these results are then extended to include tonal interference on a fm signal and intersymbol interference on a qpsk data signal

strong planning under partial observability rarely planning domains are fully observable for this reason the ability to deal with partial observability is one of the most important challenges in planning in this paper we tackle the problem of strong planning under partial observability in nondeterministic domains find a conditional plan that will result in a successful state regardless of multiple initial states nondeterministic action effects and partial observability we make the following contributions first we formally define the problem of strong planning within a general framework for modeling partially observable planning domains second we propose an effective planning algorithm based on and or search in the space of beliefs we prove that our algorithm always terminates and is correct and complete in order to achieve additional effectiveness we leverage on a symbolic bdd based representation for the domain and propose several search strategies we provide a thorough experimental evaluation of our approach based on a wide selection of benchmarks we compare the performance of the proposed search strategies and identify a uniform winner that combines heuristic distance measures with mechanisms that reduce runtime uncertainty then we compare our planner mbp with other state of the art systems mbp is able to outperform its competitor systems often by orders of magnitude

reasoning about actions with imprecise and incomplete state descriptions this article is a first step in the direction of extending possibilistic planning to account for incomplete and imprecise knowledge of the world state fundamental definitions are given and the possibilistic planning problem is recast in this new setting finally it is shown that under certain conditions possibilistic planning with imprecise and incomplete state descriptions is no harder than possibilistic planning with crisp and complete information

interleaving set temporal logic abstract a new temporal logic and interpretation are suggested which have features from linear temporal logic branching time temporal logic and partial order temporal logic the new logic can describe properties essential to the specification and correctness proofs of distributed algorithms such as those for global snapshots it is also appropriate for the justification of proof rules and for ascribing temporal semantics to properties such as layering of a program these properties cannot be described with existing temporal logics the semantic model of the logic is based on a collection of sets of interleaving sequences which reflect partial orders from the underlying semantics of the computational model for the common partial order derived from sequentiality in execution of each process the logic will distinguish between nondeterminism due to the parallel execution and nondeterminism due to local nondeterministic choices the difference in expressive power is thus qualitative and not merely due to the presence or absence of a particular temporal operator in the logic theorems are proven which clarify when it is possible to establish a property p for some of the interleaving computations and yet conclude the truth of p for every interleaving

on point based temporal disjointness abstract we address the problems of determining consistency and of finding a solution for sets of three point relations expressing exclusion of a point from an interval and for sets of four point relations expressing interval disjointness availability of these relations is an important requirement for dealing with the sorts of temporal constraints encountered in many ai applications such as plan reasoning we prove that consistency testing is np complete and finding a solution is np hard

probabilistic propositional temporal logics we present two closely related propositional probabilistic temporal logics based on temporal logics of branching time as introduced by ben ari pnueli and manna acta inform 20 1983 207 226 emerson and halpern proceedings 14th acm sympos theory of comput 1982 pp 169 179 and emerson and clarke sci comput program 2 1982 241 266 the first logic ptlf is interpreted over finite models while the second logic ptlb which is an extension of the first one is interpreted over infinite models with transition probabilities bounded away from 0 the logic ptlf allows us to reason about finite state sequential probabilistic programs and the logic ptlb allows us to reason about finite state concurrent probabilistic programs without any explicit reference to the actual values of their state transition probabilities a generalization of the tableau method yields deterministic single exponential time decision procedures for our logics and complete axiomatizations of them are given several meta results including the absence of a finite model property for ptlb and the connection between satisfiable formulae of ptlb and finite state concurrent probabilistic programs are also discussed

incremental qualitative temporal reasoning algorithms for the point algebra and the ord horn class in many applications of temporal reasoning we are interested in processing temporal information incrementally in particular given a set of temporal constraints a temporal csp and a new constraint we want to maintain certain properties of the extended temporal csp e g a solution rather than recomputing them from scratch the point algebra pa and the interval algebra ia are two well known frameworks for qualitative temporal reasoning the reasoning algorithms for pa and the tractable fragments of ia such as nebel and burckert s maximal tractable class of relations ord horn have originally been designed for static reasoning in this paper we study the incremental version of the fundamental reasoning problems in the context of these tractable classes we propose a collection of new polynomial algorithms that can amortize their complexity when processing a sequence of input constraints to incrementally decide satisfiability to maintain a solution or to update the minimal representation of the csp our incremental algorithms improve the total time complexity of using existing static techniques by a factor of o n or o n2 where n is the number of the variables involved by the temporal csp an experimental analysis focused on constraints over pa confirms the computational advantage of our incremental approach

programming in metric temporal logic abstract we present a fragment of metric temporal logic called bounded universal horn formulae as a theoretical basis for temporal reasoning in logic programming we characterize its semantics in terms of fixed points and canonical models and present an efficient proof method as operational semantics based on sld resolution with constraints although the complexity of real time logics is very high in general the validity problem for most of them is 1 l complete already for propositional fragments in case of dense time structures we show that the class of bounded universal horn formulae admits complete and efficient proof methods exploiting uniform proofs and linear time complexity of basic steps of the proof method the results obtained heavily rely on the fragment investigated and make it necessary to establish some basic results like compactness and approximation of the least model by at most steps of the corresponding fixed point operator directly without recourse to standard methods in dense case the fragment itself is sufficiently expressive for a variety of applications ranging from real time systems temporal deductive data bases and sequence evaluation purposes we show that the fragment is the greatest of the metric temporal logic in discrete and dense case having the properties classically desired for logic programming languages

convergence analysis of finite alphabet beamformers for digital cochannel signals in this paper we examine the convergence behavior of finite alphabet fa beamformers the two most popular implementations of fa beamformers for digital communication signals are the iterative least squares with projection ilsp and the minimum mean square error mmse beamformer to facilitate the analysis for the binary communications case we derive closed form expressions for the mean weight vector signal to noise ratio and signal to interference ratio for both the ilsp and mmse beamformers in terms of the bit error rate ber performance at each iteration next we generalize the analysis for the m ary pulse amplitude modulation and m ary phase shift keying cases we show that both ilsp and mmse beamformers have previously unreported bias terms in the array response vectors which are functions of the ber for each iteration furthermore as the ber becomes arbitrarily small we show that our solutions converge to the well known asymptotic expressions widely published in the literature next we provide a geometric interpretation of the effects of the noise bias vector in terms of angles between subspaces based on our analysis we were able to develop necessary and sufficient conditions for convergence in the mean we conclude with monte carlo simulations to validate our analysis

an analytical constant modulus algorithm iterative constant modulus algorithms such as godard 1980 and cma have been used to blindly separate a superposition of cochannel constant modulus cm signals impinging on an antenna array these algorithms have certain deficiencies in the context of convergence to local minima and the retrieval of all individual cm signals that are present in the channel we show that the underlying constant modulus factorization problem is in fact a generalized eigenvalue problem and may be solved via a simultaneous diagonalization of a set of matrices with this new analytical approach it is possible to detect the number of cm signals present in the channel and to retrieve all of them exactly rejecting other non cm signals only a modest amount of samples is required the algorithm is robust in the presence of noise and is tested on measured data collected from an experimental set up

a critical examination of allen s theory of action and time abstract j f allen s theory of time and action is examined and found to be unsuitable for representing facts about continuous change a series of revisions to allen s theory is proposed in order to accommodate this possibility the principal revision is a diversification of the temporal ontology to include instants on the same footing as intervals a distinction is also made between two kinds of property called states of position and states of motion with respect to the logic of their temporal incidence as a consequence of these revisions it is also found necessary to diversify the range of predicates specifying temporal location finally it is argued that allen s category of processes is superfluous since it can be assimilated with the category of properties the implications of this assimilation for the representation of sentences containing verbs in the progressive aspect are discussed

reasoning about action i a possible worlds approach abstract reasoning about change is an important aspect of commonsense reasoning and planning in this paper we describe an approach to reasoning about change for rich domains where it is not possible to anticipate all situations that might occur the approach provides a solution to the frame problem and to the related problem that it is not always reasonable to explicitly specify all of the consequences of actions the approach involves keeping a single model of the world that is updated when actions are performed the update procedure involves constructing the nearest world to the current one in which the consequences of the actions under consideration hold the way we find the nearest world is to construct proofs of the negation of the explicit consequences of the expected action and to remove a premise in each proof from the current world computationally this construction procedure appears to be tractable for worlds like our own where few things tend to change with each action or where change is regular

convergence analysis of self adaptive equalizers a theoretical analysis of self adaptive equalization for data transmission is carried out starting from known convergence results for the corresponding trained adaptive filter the development relies on a suitable ergodicity model for the sequence of observations at the output of the transmission channel thanks to the boundedness of the decision function used for data recovery it can be proved that the algorithm is bounded strong convergence results can be reached when a perfect noiseless equalizer exists the algorithm will converge to it if the eye pattern is initially open otherwise convergence may take place towards certain other stationary points of the algorithm for which domains of attraction have been defined some of them will result in a poor error rate the case of a noisy channel exhibits limit points for the algorithm that differ from those of the classical trained algorithm the stronger the noise the greater the difference is one of the principal results of this study is the proof of the stability of the usual decision feedback algorithms once the learning period is over

an algorithm for probabilistic planning abstract we define the probabilistic planning problem in terms of a probability distribution over initial world states a boolean combination of propositions representing the goal a probability threshold and actions whose effects depend on the execution time state of the world and on random chance adopting a probabilistic model complicates the definition of plan success instead of demanding a plan that provably achieves the goal we seek plans whose probability of success exceeds the threshold in this paper we present buridan an implemented least commitment planner that solves problems of this form we prove that the algorithm is both sound and complete we then explore buridan s efficiency by contrasting four algorithms for plan evaluation using a combination of analytic methods and empirical experiments we also describe the interplay between generating plans and evaluating them and discuss the role of search control in probabilistic planning

order sorted logic programming with predicate hierarchy order sorted logic has been formalized as first order logic with sorted terms where sorts are ordered to build a hierarchy called a sort hierarchy these sorted logics lead to useful expressions and inference methods for structural knowledge that ordinary first order logic lacks nitta et al pointed out that for legal reasoning a sort hierarchy or a sorted term is not sufficient to describe structural knowledge for event assertions which express facts caused at some particular time and place the event assertions are represented by predicates with n arguments i e n ary predicates and then a particular kind of hierarchy called a predicate hierarchy is built by a relationship among the predicates to deal with such a predicate hierarchy which is more intricate than a sort hierarchy nitta et al implemented a typed sorted logic programming language extended to include a hierarchy of verbal concepts corresponding to predicates however the inference system lacks a theoretical foundation because its hierarchical expressions exceed the formalization of order sorted logic in this paper we formalize a logic programming language with not only a sort hierarchy but also a predicate hierarchy this language can derive general and concrete expressions in the two kinds of hierarchies for the hierarchical reasoning of predicates we propose a manipulation of arguments in which surplus and missing arguments in derived predicates are eliminated and supplemented as discussed by allen mcdermott and shoham in research on temporal logic and as applied by nitta et al to legal reasoning if each predicate is interpreted as an event or action not as a static property then missing arguments should be supplemented by existential terms in the argument manipulation based on this we develop a horn clause resolution system extended to add inference rules of predicate hierarchies with a semantic model restricted by interpreting a predicate hierarchy the soundness and completeness of the horn clause resolution is proven

reasoning about knowledge and probability we provide a model for reasoning about knowledge and probability together we allow explicit mention of probabilities in formulas so that our language has formulas that essentially say according to agent i formula holds with probability at least b the language is powerful enough to allow reasoning about higher order probabilities as well as allowing explicit comparisons of the probabilities an agent places on distinct events we present a general framework for interpreting such formulas and consider various properties that might hold of the interrelationship between agents probability assignments at different states we provide a complete axiomatization for reasoning about knowledge and probability prove a small model property and obtain decision procedures

a decidable propositional dynamic logic with explicit probabilities a propositional version of feldman and harel s pr dl 1982 14th acm sympos on theory of computing pp 181 195 j comput system sci 28 no 2 1984 193 215 is defined and shown to be decidable in double exponential space the logic allows propositional level formulas involving probabilistic programs and contains the full quantified real number theory for dealing with probabilities the decidability proof introduces a succession of abstractions of the notion of a model from the full generality of measure theoretical models down to the finite schematic models which seem to be the most basic structures relating programs and probabilities

a temporal logic for proving properties of topologically general executions we present a generalization of the temporal propositional logic of linear time which is useful for stating and proving properties of the generic execution sequence of a parallel program or a non deterministic program the formal system we present is exactly that same as the third of three logics presented by lehmann and shelah information and control53 165 198 1982 but we give it a different semantics the models are tree models of arbitrary size similar to those used in branching time temporal logic the formulation we use allows us to state properties of the co meagre family of paths where the term co meagre refers to a set whose complement is of the first category in baire s classification looking at the set of paths in the model as a metric space our system is decidable sound and complete for models of arbitrary size but it has the finite model property namely every sentence having a model has a finite model

approximate reasoning with time abstract the need to make time dependent assumptions is frequently encountered in many systems especially in dynamical systems this paper presents a theoretical study of the representation and manipulation of some vague concepts in the temporally changing world our approach to the modeling is based on zadeh s theory of possibility we introduce a concept of similarity index in reasoning with time we present two different types of widely used models one rule based and another resolution based for reasoning with time we consider simply yet concrete examples to illustrate the usefulness of the proposed models

modeling a dynamic and uncertain world i symbolic and probabilistic reasoning about change abstract intelligent agency requires some ability to predict the future an agent must ask itself what is presently its best course of action given what it now knows about what the world will be like when it intends to act this paper presents a system that uses a probabilistic model to reason about the effects of an agent s proposed actions on a dynamic and uncertain world computing the probability that relevant propositions will hold at a specified point in time the model allows for incomplete information about the world the occurrence of exogenous unplanned events unreliable sensors and the possibility of an imperfect causal theory the system provides an application program with answers to questions of the form is the probability that will hold in the world at time t greater than it is unique among algorithms for probabilistic temporal reasoning in that it tries to limit its inference according to the proposition time and probability threshold provided by the application the system will also notify the application if subsequent evidence invalidates its answer to a query

the computational complexity of probabilistic inference using bayesian belief networks research note abstract bayesian belief networks provide a natural efficient method for representing probabilistic dependencies among a set of variables for these reasons numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks more general classes of belief networks however have eluded efforts to develop efficient inference algorithms we show that probabilistic inference using belief networks is np hard therefore it seems unlikely that an exact algorithm can be developed to perform probabilistic inference efficiently over all classes of belief networks this result suggests that research should be directed away from the search for a general efficient probabilistic inference algorithm and toward the design of efficient special case average case and approximation algorithms

the causal perturbation expansion revisited rescaling the interacting dirac sea the causal perturbation expansion defines the dirac sea in the presence of a time dependent external field it yields an operator whose image generalizes the vacuum solutions of negative energy and thus gives a canonical splitting of the solution space into two subspaces after giving a self contained introduction to the ideas and techniques we show that this operator is in general not idempotent we modify the standard construction by a rescaling procedure giving a projector on the generalized negative energy subspace the resulting rescaled causal perturbation expansion uniquely defines the fermionic projector in terms of a series of distributional solutions of the dirac equation the technical core of the paper is to work out the combinatorics of the expansion in detail it is also shown that the fermionic projector with interaction can be obtained from the free projector by a unitary transformation we finally analyze the consequences of the rescaling procedure on the light cone expansion

light cone expansion of the dirac sea in the presence of chiral and scalar potentials we study the dirac sea in the presence of external chiral and scalar pseudoscalar potentials in preparation a method is developed for calculating the advanced and retarded green s functions in an expansion around the light cone for this we first expand all feynman diagrams and then explicitly sum up the perturbation series the light cone expansion expresses the green s functions as an infinite sum of line integrals over the external potential and its partial derivatives the dirac sea is decomposed into a causal and a noncausal contribution the causal contribution has a light cone expansion which is closely related to the light cone expansion of the green s functions it describes the singular behavior of the dirac sea in terms of nested line integrals along the light cone the noncausal contribution on the other hand is to every order in perturbation theory a smooth function in position space

the complexity of probabilistic verification we determine the complexity of testing whether a finite state sequential or concurrent probabilistic program satisfies its specification expressed in linear time temporal logic for sequential programs we present an algorithm that runs in time linear in the program and exponential in the specification and also show that the problem is in pspace matching the known lower bound for concurrent programs we show that the problem can be solved in time polynomial in the program and doubly exponential in the specification and prove that it is complete for double exponential time we also address these questions for specifications described by automata or formulas in extended temporal logic

a logic of time chance and action for representing plans abstract this paper integrates logical and probabilistic approaches to the representation of planning problems by developing a first order logic of time chance and action we start by making explicit and precise commonsense notions about time chance and action central to the planning problem we then develop a logic the semantics of which incorporates these intuitive properties the logical language integrates both modal and probabilistic constructs and allows quantification over time points probability values and domain individuals probability is treated as a sentential operator in the language so it can be arbitrarily nested and combined with other logical operators the language can represent the chance that facts hold and events occur at various times it can represent the chance that actions and other events affect the future the model of action distinguishes between action feasibility executability and effects we present a proof theory for the logic and show how the logic can be used to describe actions in such a way that the action descriptions can be composed to infer properties of plans via the proof theory

knowledge belief and time abstract in the conclusion of 7 halpern and moses expressed their interest in a logical system in which one could talk about knowledge and belief and belief about knowledge knowledge about belief and so on we investigate such systems in the first part of the paper knowledge and belief without time are considered common knowledge and common belief are defined and compared a logical system and a family of models are proposed a completeness result is proved and a decision procedure described in the second part of the paper time is considered different notions of beliefs are distinguished obeying different properties of persistence one interpretation of belief which obeys a very strong persistence axiom is put forward and used in the analysis of the wise men puzzle

planning for conjunctive goals abstract the problem of achieving conjunctive goals has been central to domain independent planning research the nonlinear constraint posting approach has been most successful previous planners of this type have been complicated heuristic and ill defined i have combined and distilled the state of the art into a simple precise implemented algorithm tweak which i have proved correct and complete i analyze previous work on domain independent conjunctive planning in retrospect it becomes clear that all conjunctive planners linear and nonlinear work the same way the efficiency and correctness of these planners depends on the traditional add delete list representation for actions which drastically limits their usefulness i present theorems that suggest that efficient general purpose planning with more expressive action representations is impossible and suggest ways to avoid this problem

signal processing applications of oblique projection operators oblique projection operators are used to project measurements onto a low rank subspace along a direction that is oblique to the subspace they may be used to enhance signals while nulling interferences in the paper the authors give several basic results for oblique projections including formulas for constructing oblique projections with desired range and null space they analyze the algebra and geometry of oblique projections in order to understand their properties they then show how oblique projections can be used to separate signals from structured noise such as impulse noise damped or undamped interfering sinusoids such as power line interference and narrow band noise in some of the problems addressed the oblique projection provides an alternative way to implement an already known solution expressing these solutions as oblique projections brings geometrical insight to the study of the solution the geometry of oblique projections enables one to compute performance in terms of angles between signal and noise subspaces as a special case of removing impulse noise the authors can use oblique projections to interpolate missing data samples in array processing oblique projections can be used to simultaneously steer beams and nulls in communications oblique projections can be used to remove intersymbol interference

on primality tests whether an odd number m is prime can be decided on the knowledge of the image of the function a mapsto a m 1 2 m as a consequence an algorithm for testing primality is proposed under the extended riemann hypothesis which is more efficient than ones proposed by miller pros 7th acm symp theory of computing 1975 pp 234 239 and velu sigact news 10 1978 pp 58 59 a probabilistic version is compared with the algorithm of solovay and strassen siam j comput 6 1977 pp 84 85 erratum 7 1978 p 118

a non reified temporal logic abstract a temporal logic is presented for reasoning about propositions whose truth values might change as a function of time the temporal propositions consist of formulae in a sorted first order logic with each atomic predicate taking some set of temporal arguments as well as a set of non temporal arguments the temporal arguments serve to specify the predicate s dependence on time by partitioning the terms of the language into two sorts temporal and non temporal time is given a special syntactic and semantic status without having to resort to reification the benefits of this logic are that it has a clear semantics and a well studied proof theory unlike the first order logic presented by shoham propositions can be expressed and interpreted with respect to any number of temporal arguments not just with respect to a pair of time points an interval we demonstrate the advantages of this flexibility in addition nothing is lost by this added flexibility and more standard and usable syntax to prove this assertion we show that the logic completely subsumes shoham s temporal logic 19

maximum likelihood localization of multiple sources by alternating projection an algorithm referred to as apm for computing the maximum likelihood estimator of the locations of simple sources in passive sensor arrays is presented the algorithm is equally applicable to the case of coherent signals and to the case of a single snapshot the algorithm is iterative the maximum of the likelihood function is computed by successive approximations the convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios the key to this global convergence is the initialization scheme

perturbative description of the fermionic projector normalization causality and furry s theorem the causal perturbation expansion of the fermionic projector is performed with a contour integral method different normalization conditions are analyzed it is shown that the corresponding light cone expansions are causal in the sense that they only involve bounded line integrals for the resulting loop diagrams we prove a generalized furry theorem

complexity of finite horizon markov decision process problems controlled stochastic systems occur in science engineering manufacturing social sciences and many other cntexts if the systems is modeled as a markov decision process mdp and will run ad infinitum the optimal control policy can be computed in polynomial time using linear programming the problems considered here assume that the time that the process will run is finite and based on the size of the input there are mny factors that compound the complexity of computing the optimal policy for instance there are many factors that compound the complexity of this computation for instance if the controller does not have complete information about the state of the system or if the system is represented in some very succint manner the optimal policy is provably not computable in time polynomial in the size of the input we analyze the computational complexity of evaluating policies and of determining whether a sufficiently good policy exists for a mdp based on a number of confounding factors including the observability of the system state the succinctness of the representation the type of policy even the number of actions relative to the number of states in almost every case we show that the decision problem is complete for some known complexity class some of these results are familiar from work by papadimitriou and tsitsiklis and others but some such as our pl completeness proofs are surprising we include proofs of completeness for natural problems in the as yet little studied classes np pp

stochastic dynamic programming with factored representations markov decision processes mdps have proven to be popular models for decision theoretic planning but standard dynamic programming algorithms for solving mdps rely on explicit state based specifications and computations to alleviate the combinatorial problems associated with such methods we propose new representational and computational techniques for mdps that exploit certain types of problem structure we use dynamic bayesian networks with decision trees representing the local families of conditional probability distributions to represent stochastic actions in an mdp together with a decision tree representation of rewards based on this representation we develop versions of standard dynamic programming algorithms that directly manipulate decision tree representations of policies and value functions this generally obviates the need for state by state computation aggregating states at the leaves of these trees and requiring computations only for each aggregate state the key to these algorithms is a decision theoretic generalization of classic regression analysis in which we determine the features relevant to predicting expected value we demonstrate the method empirically on several planning problems showing significant savings for certain types of domains we also identify certain classes of problems for which this technique fails to perform well and suggest extensions and related ideas that may prove useful in such circumstances we also briefly describe an approximation scheme based on this approach

optimal control of infinite horizon partially observable decision processes modelled as generators of probabilistic regular languages decision processes with incomplete state feedback have been traditionally modelled as partially observable markov decision processes in this article we present an alternative formulation based on probabilistic regular languages the proposed approach generalises the recently reported work on language measure theoretic optimal control for perfectly observable situations and shows that such a framework is far more computationally tractable to the classical alternative in particular we show that the infinite horizon decision problem under partial observation modelled in the proposed framework is approximable and in general is not harder to solve compared to the fully observable case the approach is illustrated via two simple examples

nonmonotonic logic and temporal projection abstract nonmonotonic formal systems have been proposed as an extension to classical first order logic that will capture the process of human default reasoning or plausible inference through their inference mechanisms just as modus ponens provides a model for deductive reasoning but although the technical properties of these logics have been studied in detail and many examples of human default reasoning have been identified for the most part these logics have not actually been applied to practical problems to see whether they produce the expected results we provide axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning thus presumably amenable to representation in nonmonotonic logic upon examining the resulting nonmonotonic theories however we find that the inferences permitted by the logics are not those we had intended when we wrote the axioms and in fact are much weaker this problem is shown to be independent of the logic used nor does it depend on any particular temporal representation upon analyzing the failure we find that the nonmonotonic logics we considered are inherently incapable of representing this kind of default reasoning the first part of the paper is an expanded version of one that appeared in the 1986 aaai proceedings the second part reports on several responses to our result that have appeared since the original paper was published

reasoning about partially ordered events abstract this paper describes a class of temporal reasoning problems involving events whose order is not completely known we examine the complexity of such problems and show that for all but trivial cases these problems are likely to be intractable as an alternative to a complete but potentially exponential time decision procedure we provide a partial decision procedure that reports useful results and runs in polynomial time

planning and acting in partially observable stochastic domains in this paper we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains we begin by introducing the theory of markov decision processes mdps and partially observable mdps pomdps we then outline a novel algorithm for solving pomdps off line and show how in some cases a finite memory controller can be extracted from the solution to a pomdp we conclude with a discussion of how our approach relates to previous work the complexity of finding exact solutions to pomdps and of some possibilities for finding approximate solutions

contingent planning under uncertainty via stochastic satisfiability we describe a new planning technique that efficiently solves probabilistic propositional contingent planning problems by converting them into instances of stochastic satisfiability ssat and solving these problems instead we make fundamental contributions in two areas the solution of ssat problems and the solution of stochastic planning problems this is the first work extending the planning as satisfiability paradigm to stochastic domains our planner zander can solve arbitrary goal oriented finite horizon partially observable markov decision processes pomdps an empirical study comparing zander to seven other leading planners shows that its performance is competitive on a range of problems

blind separation of synchronous co channel digital signals using an antenna array i algorithms we propose a maximum likelihood ml approach for separating and estimating multiple synchronous digital signals arriving at an antenna array at a cell site the spatial response of the array is assumed to be known imprecisely or unknown we exploit the finite alphabet property of digital signals to simultaneously estimate the array response and the symbol sequence for each signal uniqueness of the estimates is established for bpsk signals we introduce a signal detection technique based on the finite alphabet property that is different from a standard linear combiner computationally efficient algorithms for both block and recursive estimation of the signals are presented this new approach is applicable to an unknown array geometry and propagation environment which is particularly useful in wireless communication systems simulation results demonstrate its promising performance

reasoning with time and chance the temporal propositional logic of linear time is generalized to an uncertain world in which random events may occur the formulas do not mention probabilities explicitly i e the only probability appearing explicitly in formulas is probability one this logic is claimed to be useful for stating and proving properties of probabilistic programs it is convenient for proving those properties that do not depend on the specific distribution of probabilities used in the program s random draws the formulas describe properties of execution sequences the models are stochastic systems with state transition probabilities three different axiomatic systems are proposed and shown complete for general models finite models and models with bounded transition probabilities respectively all three systems are decidable by the results of rabin trans amer math soc 141 1969 1 35

towards a general theory of action and time abstract a formalism for reasoning about actions is proposed that is based on a temporal logic it allows a much wider range of actions to be described than with previous approaches such as the situation calculus this formalism is then used to characterize the different types of events processes actions and properties that can be described in simple english sentences in addressing this problem we consider actions that involve non activity as well as actions that can only be defined in terms of the beliefs and intentions of the actors finally a framework for planning in a dynamic world with external events and multiple agents is suggested

on the undecidability of probabilistic planning and related stochastic optimization problems automated planning the problem of how an agent achieves a goal given a repertoire of actions is one of the foundational and most widely studied problems in the ai literature the original formulation of the problem makes strong assumptions regarding the agent s knowledge and control over the world namely that its information is complete and correct and that the results of its actions are deterministic and known recent research in planning under uncertainty has endeavored te relax these assumptions providing formal and computation models wherein the agent has incomplete or noisy information about the world and has noisy sensors and effectors this research has mainly taken one of two approaches extend the classical planning paradigm to a semantics that admits uncertainty or adopt another framework for approaching the problem most commonly the markov decision process mdp model this paper presents a complexity analysis of planning under uncertainty it begins with the probabilistic classical planning problem showing that problem to be formally undecidable this fundamental result is then applied to a broad class of stochastic optimization problems in brief any problem statement where the agent a operates over an infinite or indefinite time horizon and b has available only probabilistic information about the system s state undecidability is established for policy existence problems for partially observable infinite horizon markov decision processes under discounted and undiscounted total reward models average reward models and state avoidance models the results also apply to corresponding approximation problems with undiscounted objective functions the paper answers a significant open question raised by papadimitriou and tsitsiklis math oper res 12 3 1987 441 450 about the complexity of infinite horizon pomdps

applications of circumscription to formalizing common sense knowledge we present a new and more symmetric version of the circumscription method of nonmonotonic reasoning first described in mccarthy 1980 and some applications to formalizing common sense knowledge the applications in this paper are mostly based on minimizing the abnormality of different aspects of various entities included are nonmonotonic treatments of is a hierarchies the unique names hypothesis and the frame problem the new circumscription may be called formula circumscription to distinguish it from the previously defined domain circumscription and predicate circumscription a still more general formalism called prioritized circumscription is briefly explored

defining fairness in reactive and concurrent systems we define when a linear time temporal property is a fairness property with respect to a given system this captures the essence shared by most fairness assumptions that are used in the specification and verification of reactive and concurrent systems such as weak fairness strong fairness k fairness and many others we provide three characterizations of fairness a language theoretic a game theoretic and a topological characterization it turns out that the fairness properties are the sets that are large from a topological point of view that is they are the co meager sets in the natural topology of runs of a given system this insight provides a link to probability theory where a set is large when it has measure 1 while these two notions of largeness are similar they do not coincide in general however we show that they coincide for regular properties and bounded borel measures that is an regular temporal property of a finite state system has measure 1 under a bounded borel measure if and only if it is a fairness property with respect to that system the definition of fairness leads to a generic relaxation of correctness of a system in linear time semantics we define a system to be fairly correct if there exists a fairness assumption under which it satisfies its specification equivalently a system is fairly correct if the set of runs satisfying the specification is topologically large we motivate this notion of correctness and show how it can be verified in a system

backtracking algorithms for disjunctions of temporal constraints abstract we extend the framework of simple temporal problems studied originally by dechter meiri and pearl to consider constraints of the form x 1 y 1 r 1 x n y n r n where x 1 x n y 1 y n are variables ranging over the real numbers r 1 r n are real constants and n 1 this is a wide class of temporal constraints that can be used to model a variety of problems in temporal reasoning scheduling planning and temporal constraint databases we have implemented several progressively more efficient algorithms for the consistency checking problem for this class of temporal constraints backtracking backjumping three variations of forward checking and forward checking with backjumping we have partially ordered the above algorithms according to the number of visited search nodes and the number of performed consistency checks although our problem is non binary our results agree with the results of kondrak and van beek who consider only binary constraints we have also studied the performance of the above algorithms experimentally using randomly generated sets of data and job shop scheduling problems the experiments with random instances allowed us to locate the hard region for this class of problems the results show that hard problems occur at a critical value of the ratio of disjunctions to variables

performance analysis of the minimum variance beamformer in the presence of steering vector errors we present an analysis of the signal to interference plus noise ratio sinr at the output of the minimum variance beamformer in the presence of steering vector errors the analysis yields an explicit expression for the sinr in terms of the variance of the steering vector errors and the other parameters affecting performance including signal to noise ratio snr interference to noise ratio inr signal to interference ratio sir angular separation between desired signal and interference array size and shape correlation between desired signal and interference and finite sample size

equivalence notions and model minimization in markov decision processes many stochastic planning problems can be represented using markov decision processes mdps a difficulty with using these mdp representations is that the common algorithms for solving them run in time polynomial in the size of the state space where this size is extremely large for most real world planning problems of interest recent ai research has addressed this problem by representing the mdp in a factored form factored mdps however are not amenable to traditional solution methods that call for an explicit enumeration of the state space one familiar way to solve mdp problems with very large state spaces is to form a reduced or aggregated mdp with the same properties as the original mdp by combining equivalent states in this paper we discuss applying this approach to solving factored mdp problems we avoid enumerating the state space by describing large blocks of equivalent states in factored form with the block descriptions being inferred directly from the original factored representation the resulting reduced mdp may have exponentially fewer states than the original factored mdp and can then be solved using traditional methods the reduced mdp found depends on the notion of equivalence between states used in the aggregation the notion of equivalence chosen will be fundamental in designing and analyzing algorithms for reducing mdps optimally these algorithms will be able to find the smallest possible reduced mdp for any given input mdp and notion of equivalence i e find the minimal model for the input mdp unfortunately the classic notion of state equivalence from non deterministic finite state machines generalized to mdps does not prove useful we present here a notion of equivalence that is based upon the notion of bisimulation from the literature on concurrent processes our generalization of bisimulation to stochastic processes yields a non trivial notion of state equivalence that guarantees the optimal policy for the reduced model immediately induces a corresponding optimal policy for the original model with this notion of state equivalence we design and analyze an algorithm that minimizes arbitrary factored mdps and compare this method analytically to previous algorithms for solving factored mdps we show that previous approaches implicitly derive equivalence relations that we define here

reasoning about temporal relations a maximal tractable subclass of allen s interval algebra we introduce a new subclass of allen s interval algebra we call ord horn subclass which is a strict superset of the pointisable subclass we prove that reasoning in the ord horn subclass is a polynomial time problem and show that the path consistency method is sufficient for deciding satisfiability further using an extensive machine generated case analysis we show that the ord horn subclass is a maximal tractable subclass of the full algebra assuming p np in fact it is the unique greatest tractable subclass amongst the subclasses that contain all basic relations

fuzzy constraint networks for signal pattern recognition this paper deals with representation and reasoning on information concerning the evolution of a physical parameter by means of a model based on the fuzzy constraint satisfaction problem formalism and with which it is possible to define what we call fuzzy temporal profiles ftp based on fundamentally linguistic information this model allows the integration of knowledge on the evolution of a set of parameters into a knowledge representation scheme in which time plays a fundamental role the ftp model describes the behaviour of a physical parameter on the basis of a set of signal events and which allows the evolution of the parameter between each pair of events to be modelled as signal episodes given the fundamentally linguistic nature of the information represented the consistency analysis of this information is an essential task nevertheless the obtention of the minimal representation of the network that defines an ftp is an np hard problem in spite of this we supply algorithms guaranteeing local levels of consistency that allow to correct a large proportion of the errors committed by a human expert in the linguistic description of the profile furthermore we propose a new topology whose consistency can be guaranteed in polynomial time we also study the applicability of this model in the recognition of signal patterns