Mining HCI Data for Theory of Mind Induction

Human-computer interaction (HCI) results in enormous amounts of data-bearing potentials for understanding a human user’s intentions, goals, and desires. Knowing what users want and need is a key to intelligent system assistance. The theory of mind concept known from studies in animal behavior is adopted and adapted for expressive user modeling. Theories of mind are hypothetical user models representing, to some extent, a human user’s thoughts. A theory of mind may even reveal tacit knowledge. In this way, user modeling becomes knowledge discovery going beyond the human’s knowledge and covering domain-specific insights. Theories of mind are induced by mining HCI data. Data mining turns out to be inductive modeling. Intelligent assistant systems inductively modeling a human user’s intentions, goals, and the like, as well as domain knowledge are, by nature, learning systems. To cope with the risk of getting it wrong, learning systems are equipped with the skill of reflection

On case-based learnability of languages

Case-based reasoning is deemed an important technology to alleviate the bottleneck of knowledge acquisition in Artificial Intelligence (AI). In case-based reasoning, knowledge is represented in the form of particular cases with an appropriate similarity measure rather than any form of rules. The case-based reasoning paradigm adopts the view that an Al system is dynamically changing during its life-cycle which immediately leads to learning considerations. Within the present paper, we investigate the problem of case-based learning of indexable classes of formal languages. Prior to learning considerations, we study the problem of case-based representability and show that every indexable class is case-based representable with respect to a fixed similarity measure. Next, we investigate several models of case-based learning and systematically analyze their strengths as well as their limitations. Finally, the general approach to case-based learnability of indexable classes of formal languages is prototypically applied to so-called containmet decision lists, since they seem particularly tailored to case-based knowledge processing

In search for the human factor in rule based game AI: the GrinTu evaluation and refinement approach

What is the biggest difference between playing a game against a human or against a computer generated player? Why do many people believe it is more challenging to play with humans than playing with an artificial player? The big success of massive multiplayer games and the huge number of so-called “LAN parties”, where players meet and play with each other, seems to be related to the human demeanor of the players. All this indicates, that the current state of game AI is unsatisfactory compared to the performance of human players. This paper introduces a tool for analyzing basic computer games with incorporated AI modules which store strategies for performing the behavior of artificial players. This sets the stage for a systematic evaluation and refinement of rule based game AI

Kārawān, 1347-09-19, 1968-12-10

The volume and issue of this edition is Kārawān, v. 1, no. 66. The numbers in the title refer to the date of the edition, with the first set of numbers representing the Persian calendar and the second set of numbers representing the date in the Gregorian calendar. Editor: 1968- Abdul Haq Waleh. Title transliterated into English : Caravan. Lumṛay kal̄, Shpeg shpetuma ganạh [vol. 1, no. 66] (Qaws. 19, 1347 [December 10, 1968]).https://digitalcommons.unomaha.edu/karawan/1069/thumbnail.jp

In search for the human factor in rule based game AI: the GrinTu evaluation and refinement approach

What is the biggest difference between playing a game against a human or against a computer generated player? Why do many people believe it is more challenging to play with humans than playing with an artificial player? The big success of massive multiplayer games and the huge number of so-called “LAN parties”, where players meet and play with each other, seems to be related to the human demeanor of the players. All this indicates, that the current state of game AI is unsatisfactory compared to the performance of human players. This paper introduces a tool for analyzing basic computer games with incorporated AI modules which store strategies for performing the behavior of artificial players. This sets the stage for a systematic evaluation and refinement of rule based game AI