Characterizing perfect recall using next-step temporal operators in S5 and sub-S5 Epistemic Temporal Logic

We review the notion of perfect recall in the literature on interpreted systems, game theory, and epistemic logic. In the context of Epistemic Temporal Logic (ETL), we give a (to our knowledge) novel frame condition for perfect recall, which is local and can straightforwardly be translated to a defining formula in a language that only has next-step temporal operators. This frame condition also gives rise to a complete axiomatization for S5 ETL frames with perfect recall. We then consider how to extend and consolidate the notion of perfect recall in sub-S5 settings, where the various notions discussed are no longer equivalent

Measuring Uncertainty in Games: Design and Preliminary Validation

Uncertainty is an important element of game play, which is widely believed to act as a precondition for player experience (PX). To investigate the concept and examine its relation to other PX concepts, we should be able to measure it. We present the design and preliminary results of the validation of the Player Uncertainty in Games (PUG) questionnaire. Based on various sources from games user research and work done with regards to searching digital archives, we designed a questionnaire that measures the experience of uncertainty in games. The scale was refined down to 66 items via interviews with players and expert reviews, which was then validated and further refined based on data gathered from gamers in an online survey. The Principal Component Analysis showed high level of internal consistency for the scale and each of its four factors: Disorientation, Exploration, Prospect, and Randomness. This work demonstrates the initial findings towards a validated tool for measuring uncertainty of players in digital games

Timing of Messages and the Aumann Conjecture: A multiple-Selves Approach

The Aumann (1990) conjecture states that cheap-talk messages do not necessarily help to coordinate on efficient Nash equilibria. In an experimental test of Aumann’s conjecture, Charness (2000) found that cheap-talk messages facilitate coordination when they precede the action, but not when they follow the action. Standard game-theoretical modeling abstracts from this timing effect, and therefore cannot account for it. To allow for a formal analysis of the timing effect, I study the sequential equilibria of the signaling game in which the sender is modeled as comprising two selves: an acting self and a signaling self. I interpret Aumann’s argument in this context to imply that all of the equilibria in this game are ‘babbling’ equilibria, in which the message conveys no information and does not affect the behavior of the receiver. Using this framework, I show that a fully communicative equilibrium exists—only if the message precedes the action but not when the message follows the action. In the latter case, no information is transmitted in any equilibrium. This result provides a game-theoretical explanation for the puzzling experimental results obtained by Charness (2000). I discuss other explanations for this timing-of-message effect and their relationship to the current analysis.pre-play communication, Nash equilibrium, coordination games, multiple selves

Imperfect Recall and Time Inconsistencies: An experimental test of the absentminded driver "paradox"

Absentmindedness is a special case of imperfect recall which according to Piccione and Rubinstein (1997a) leads to time inconsistencies. Aumann, Hart and Perry (1997a) question their argument and show how dynamic inconsistencies can be resolved. The present paper explores this issue from a descriptive point of view by examining the behavior of absentminded individuals in a laboratory environment. Absentmindedness is manipulated in two ways. In one treatment, it is induced by cognitively overloading participants. In the other, it is imposed by randomly matching decisions with decision nodes in the information set. The results provide evidence for time inconsistencies in all treatments. We introduce a behavioral principal, which best explains the data.imperfect recall, absentmindedness, dynamic inconsistency, experiment

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge