Strategy Synthesis for Autonomous Agents Using PRISM

We present probabilistic models for autonomous agent search and retrieve missions derived from Simulink models for an Unmanned Aerial Vehicle (UAV) and show how probabilistic model checking and the probabilistic model checker PRISM can be used for optimal controller generation. We introduce a sequence of scenarios relevant to UAVs and other autonomous agents such as underwater and ground vehicles. For each scenario we demonstrate how it can be modelled using the PRISM language, give model checking statistics and present the synthesised optimal controllers. We conclude with a discussion of the limitations when using probabilistic model checking and PRISM in this context and what steps can be taken to overcome them. In addition, we consider how the controllers can be returned to the UAV and adapted for use on larger search areas

Evaluating three frameworks for the value of information: adaptation to task characteristics and probabilistic structure

We identify, and provide an integration of, three frameworks for measuring the informativeness of cues in a multiple-cue judgment task. Cues can be ranked by information value according to expected information gain (Bayesian framework), cue-outcome correlation (Correlational framework), or ecological validity (Ecological framework). In three experiments, all frameworks significantly predicted information acquisition, with the Correlational (then the Bayesian) framework being most successful. Additionally, participants adapted successfully to task characteristics (cue cost, time pressure, and information limitations) – altering the gross amount of information acquired, but not responding to more subtle features of the cues’ information value that would have been beneficial. Rational analyses of our task environments indicate that participants' behavior can be considered successful from a boundedly rational standpoint

How Distinct are Intuition and Deliberation? An Eye-Tracking Analysis of Instruction-Induced Decision Modes

In recent years, numerous studies comparing intuition and deliberation have been published. However, until now relatively little is known about the cognitive processes underlying the two decision modes. Therefore, we analyzed processes of information search and integration using eye-tracking technology. We tested hypotheses derived from dual-process models which postulate that intuition and deliberation are completely distinct processes against predictions of interventionist models. The latter assume that intuitive and deliberate decisions are based on the same basic process which is supplemented by additional processes in the deliberate decision mode. We manipulated decision mode between-participants by means of instructions and participants completed simple and complex city-size tasks as well as complex legal inference tasks. Our findings indicate that the instruction to deliberate does not necessarily increase levels of processing. We found no difference in mean fixation duration and the distribution of short, medium and long fixations. Instruction-induced deliberation led to a higher number of fixations, a more complete information search and more repeated information investigations. Overall, the data support interventionist models suggesting that decisions mainly rely on automatic processes which are supplemented by additional operations in the deliberate decision mode.Decision Making, Decision Mode, Intuition, Deliberation, Eye-Tracking

Re-visions of rationality?

Empirical evidence suggests proponents of the ‘adaptive toolbox’ framework of human judgment need to rethink their vision of rationality