Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot. 1 Introduction A robot that delivers items and performs errands in an office environment needs to be able to navigate robustly. It should be able to overcome errors in perception and action, at worst getting lost for some period of time, but then being able to recover by re-localizing itself and continuing with its task. The Bayesian framework is particularly appropriate for modeling the robot's belief about its location (or, more generally, the state of the world). It suppl...

We show how to exploit symmetry in model checking for concurrent systems containing many identical or isomorphic components. We focus in particular on those composed of many isomorphic processes. In many cases we are able to obtain significant, even exponential, savings in the complexity of model checking. 1 Introduction In this paper, we show how to exploit symmetry in model checking. We focus on systems composed of many identical (isomorphic) processes. The global state transition graph M of such a system exhibits a great deal of symmetry, characterized by the group of graph automorphisms of M. The basic idea underlying our method is to reduce model checking over the original structure M, to model checking over a smaller quotient structure M, where symmetric states are identified. In the following paragraphs, we give a more detailed but still informal account of a "group-theoretic" approach to exploiting symmetry. More precisely, the symmetry of M is reflected in the group, Aut M...

This paper describes a method for generating test sequences for checking the conformance of a protocol implementation to its specification. A Rural Chinese Postman Tour is used to determine a minimum-cost tour of the transition graph of a finite-state machine. When used in combination with Unique Input/Output Sequences [18], the technique yields an efficient method for computing a test sequence for protocol conformance testing.

As intelligent, autonomous systems are embedded in critical real-world environments, it becomes increasingly important to rigorously characterize how these systems will perform. Research in real-time computing and control has developed ways of proving that a given control system will meet the demands of an environment, but has not addressed the dynamic planning of control actions. Building an agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. The Cooperative Intelligent Real-time Control Architecture (CIRCA) implements this blending by executing complex AI methods and guaranteed real-time control plans on separate subsystems. We describe the formal model of agent/environment interactions that CIRCA uses to build control plans, and we show how those control plans are guaranteed to meet domain requirements. CIRCA's world model provides the information required to make real-time performance guarantees, but avo...

Exploring and mapping an unknown environment is a fundamental problem, which is studied in various contexts. Many works have focused on finding efficient solutions to restricted versions of the problem. In this paper, we consider a model that makes very limited assumptions on the environment and solve the mapping problem in this general setting. We model

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Abstract. We present new procedures for inferring the structure of a finite-state automaton (FSA) from its input \ output behavior, using access to the automaton to perform experiments. Our procedures use a new representation for finite automata, based on the notion of equivalence between tesfs. We call the number of such equivalence classes the diLersL@of the automaton; the diversity may be as small as the logarithm of the number of states of the automaton. For the special class of pennatatton aatornata, we describe an inference procedure that runs in time polynomial in the diversity and log(l/6), where 8 is a given upper bound on the probability that our procedure returns an incorrect result. (Since our procedure uses randomization to perform experiments, there is a certain controllable chance that it will return an erroneous result.) We also discuss techniques for handling more general automata. We present evidence for the practical efficiency of our approach. For example, our procedure is able to infer the structure of an automaton based on Rubik’s Cube (which has approximately 10 lY states) in about 2 minutes on a DEC MicroVax. This automaton is many orders of magnitude larger than possible with previous techniques, which would require time proportional at least to the number of global states. (Note that in this example, only a small fraction (10-14, of the global

Optimized solvers for the Boolean Satisfiability (SAT) problem have many applications in areas such as hardware and software verification, FPGA routing, planning, etc. Further uses are complicated by the need to express "counting constraints" in conjunctive normal form (CNF). Expressing such constraints by pure CNF leads to more complex SAT instances. Alternatively, those constraints can be handled by Integer Linear Programming (ILP), but generic ILP solvers may ignore the Boolean nature of 0-1 variables. Therefore specialized 0-1 ILP solvers extend SAT solvers to handle these so-called "pseudo-Boolean" constraints. This work

We present a generalization of the classical theory of testing for Mealy machines to a setting of dense real-time systems. A model of timed I/O automata is introduced, inspired by the timed automaton model of Alur and Dill, together with a notion of test sequence for this model. Our main contribution is a test generation algorithm for black-box conformance testing of timed I/O automata. Although it is highly exponential and cannot be claimed to be of practical value, it is the first algorithm that yields a finite and complete set of tests for dense real-time systems.

We show that two cooperating robots can learn ex-actly any strongly-connected directed graph with n in-distinguishable nodes in expected tame polynomial in n. We introduce a new type of homing sequence for two robots which helps the robots recognize certain previously-seen nodes. We then present an algorithm in which the robots learn the graph and the homing se-quence simultaneously by wandering actively through the graph. Unlike most previous learning results us-ang homing sequences, our algorithm does not require a teacher to provide counterexamples. Furthermore, the algorithm can use efficiently any additional infor-mation available that distinguishes nodes. We also present an algorithm in which the robots learn by tak-ing random walks. The rate at which a random walk converges to the stationary distribution is character-ized by the conductance of the graph. Our random-walk algorithm learns in expected time polynomial in n and in the inverse of the conductance and is more eficient than the homing-sequence algorithm for high-conductance graphs. 1