Model Checking One-clock Priced Timed Automata

We consider the model of priced (a.k.a. weighted) timed automata, an extension of timed automata with cost information on both locations and transitions, and we study various model-checking problems for that model based on extensions of classical temporal logics with cost constraints on modalities. We prove that, under the assumption that the model has only one clock, model-checking this class of models against the logic WCTL, CTL with cost-constrained modalities, is PSPACE-complete (while it has been shown undecidable as soon as the model has three clocks). We also prove that model-checking WMTL, LTL with cost-constrained modalities, is decidable only if there is a single clock in the model and a single stopwatch cost variable (i.e., whose slopes lie in {0,1}).Comment: 28 page

Information Foraging for Perceptual Decisions

We tested an information foraging framework to characterize the mechanisms that drive active (visual) sampling behavior in decision problems that involve multiple sources of information. Experiments 1 through 3 involved participants making an absolute judgment about the direction of motion of a single random dot motion pattern. In Experiment 4, participants made a relative comparison between 2 motion patterns that could only be sampled sequentially. Our results show that: (a) Information (about noisy motion information) grows to an asymptotic level that depends on the quality of the information source; (b) The limited growth is attributable to unequal weighting of the incoming sensory evidence, with early samples being weighted more heavily; (c) Little information is lost once a new source of information is being sampled; and (d) The point at which the observer switches from 1 source to another is governed by online monitoring of his or her degree of (un)certainty about the sampled source. These findings demonstrate that the sampling strategy in perceptual decision-making is under some direct control by ongoing cognitive processing. More specifically, participants are able to track a measure of (un)certainty and use this information to guide their sampling behavior

Approximate Model Checking of Real-Time Systems for Linear Duration Invariants

Real-time systems are usually modelled with timed automata and real-time requirements relating to the state durations of the system are often specifiable using Linear Duration Invariants, which is a decidable subclass of Duration Calculus formulas. Various algorithms have been developed to check timed automata or real-time automata for linear duration invariants, but each needs complicated preprocessing and exponential calculation. To the best of our knowledge, these algorithms have not been implemented. In this paper, we present an approximate model checking technique based on a genetic algorithm to check real-time automata for linear durration invariants in reasonable times. Genetic algorithm is a good optimization method when a problem needs massive computation and it works particularly well in our case because the fitness function which is derived from the linear duration invariant is linear. ACM Computing Classification System (1998): D.2.4, C.3

A constraint-based approach to quality assurance in service choreographies.

Knowledge about the quality characteristics (QoS) of service com- positions is crucial for determining their usability and economic value. Ser- vice quality is usually regulated using Service Level Agreements (SLA). While end-to-end SLAs are well suited for request-reply interactions, more complex, decentralized, multiparticipant compositions (service choreographies) typ- ically involve multiple message exchanges between stateful parties and the corresponding SLAs thus encompass several cooperating parties with interde- pendent QoS. The usual approaches to determining QoS ranges structurally (which are by construction easily composable) are not applicable in this sce- nario. Additionally, the intervening SLAs may depend on the exchanged data. We present an approach to data-aware QoS assurance in choreographies through the automatic derivation of composable QoS models from partici- pant descriptions. Such models are based on a message typing system with size constraints and are derived using abstract interpretation. The models ob- tained have multiple uses including run-time prediction, adaptive participant selection, or design-time compliance checking. We also present an experimen- tal evaluation and discuss the benefits of the proposed approach

Strategic R&D with Knowledge Spillovers and Endogenous Time to Complete

It is shown that asymmetry in R&D efficiency between firms is an important factor determining feasibility of the preemption and attrition scenarios in competitive R&D with time to build. Scenarios of attrition and preemption games are most likely to occur when competitors have similar R&D efficiencies. In case of largely asymmetric firms the games of attrition and preemption are very unlikely, thus the R&D duration choices of firms are determined by the actual trade-off between the benefits of earlier innovation and the costs of faster R&D project completion.R&D Investment;Competition;Preemption;Attrition

Sub-Optimal Allocation of Time in Sequential Movements

The allocation of limited resources such as time or energy is a core problem that organisms face when planning complex actions. Most previous research concerning planning of movement has focused on the planning of single, isolated movements. Here we investigated the allocation of time in a pointing task where human subjects attempted to touch two targets in a specified order to earn monetary rewards. Subjects were required to complete both movements within a limited time but could freely allocate the available time between the movements. The time constraint presents an allocation problem to the subjects: the more time spent on one movement, the less time is available for the other. In different conditions we assigned different rewards to the two tokens. How the subject allocated time between movements affected their expected gain on each trial. We also varied the angle between the first and second movements and the length of the second movement. Based on our results, we developed and tested a model of speed-accuracy tradeoff for sequential movements. Using this model we could predict the time allocation that would maximize the expected gain of each subject in each experimental condition. We compared human performance with predicted optimal performance. We found that all subjects allocated time sub-optimally, spending more time than they should on the first movement even when the reward of the second target was five times larger than the first. We conclude that the movement planning system fails to maximize expected reward in planning sequences of as few as two movements and discuss possible interpretations drawn from economic theory