Substitutability, interchangeability and related concepts in Constraint Programming were introduced approximately twenty years ago and have given rise to considerable subsequent research. We survey this work, classify, and relate the different concepts, and indicate directions for future work, in particular with respect to making connections with research into symmetry breaking. This paper is a condensed version of a larger work in progress.

We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoretic framework. Our approach is compared with related work by Robins and others: in particular, we show how Robins's 'G-computation' algorithm arises naturally from this decision-theoretic perspective. Careful attention is paid to the mathematical and substantive conditions required to justify the use of this formula. These conditions revolve around a property we term stability, which relates the probabilistic behaviours of observational and interventional regimes. We show how an assumption of 'sequential randomization' (or 'no unmeasured confounders'), or an alternative assumption of 'sequential irrelevance', can be used to infer stability. Probabilistic influence diagrams are used to simplify manipulations, and their power and limitations are discussed. We compare our approach with alternative formulations based on causal DAGs or potential response models. We aim to show that formulating the problem of assessing dynamic treatment strategies as a problem of decision analysis brings clarity, simplicity and generality.

In this book we introduce a mathematically formalized concept of emotion, robot's education and other psychological parameters of intelligent robots. We also introduce unitless coefficients characterizing an emotional memory of a robot. Besides, the effect of a robot's memory upon its emotional behavior is studied, and theorems defining fellowship and conflicts in groups of robots are proved. Also unitless parameters describing emotional states of those groups are introduced, and a rule of making alternative (binary) decisions based on emotional selection is given. We introduce a concept of equivalent educational process for robots and a concept of efficiency coefficient of an educational process, and suggest an algorithm of emotional contacts within a group of robots. And generally, we present and describe a model of a virtual reality with emotional robots. The book is meant for mathematical modeling specialists and emotional robot software developers.

Diagrammatic reasoning (DR) is pervasive in human problem solving as a powerful adjunct to symbolic reasoning based on language-like representations. The research reported in this paper is a contribution to building a general purpose DR system as an extension to a SOAR-like problem solving architecture. The work is in a framework in which DR is modeled as a process where subtasks are solved, as appropriate, either by inference from symbolic representations or by interaction with a diagram, i.e., perceiving specified information from a diagram or modifying/creating objects in a diagram in specified ways according to problem solving needs. The perceptions and actions in most DR systems built so far are hand-coded for the specific application, even when the rest of the system is built using the general architecture. The absence of a general framework for executing perceptions/actions poses as a major hindrance to using them opportunistically -- the essence of open-ended search in problem solving. Our goal is to develop a framework for executing a wide variety of specified perceptions and actions across tasks/domains without human intervention. We observe that the domain/task-specific visual perceptions/actions can be transformed into domain/task-independent spatial problems. We specify a spatial problem as a quantified constraint satisfaction problem in the real domain using an open-ended vocabulary of properties, relations and actions involving three kinds of diagrammatic objects -- points, curves, regions. Solving a spatial problem from this specification requires computing the equivalent simplified quantifier-free expression, the complexity of which is inherently doubly exponential. We represent objects as configuration of simple elements to facilitate decomposition of complex problems into simpler and similar subproblems. We show that, if the symbolic solution to a subproblem can be expressed concisely, quantifiers can be eliminated from spatial problems in low-order polynomial time using similar previously solved subproblems. This requires determining the similarity of two problems, the existence of a mapping between them computable in polynomial time, and designing a memory for storing previously solved problems so as to facilitate search. The efficacy of the idea is shown by time complexity analysis. We demonstrate the proposed approach by executing perceptions and actions involved in DR tasks in two army applications.

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal algorithms for MKL (as well as for group-lasso and variants thereof), which overcomes that drawback. We show regret, convergence, and generalization bounds for the proposed method. Experiments on handwriting recognition and dependency parsing testify for the successfulness of the approach.

So, we have a variety of people here with different interests and backgrounds that I asked to talk about not just the key challenges ahead but potential opportunities and promising pathways, trajectories to solving those problems, and their predictions about how R&D might proceed in terms of the timing of various kinds of development over time. I asked the panelists briefly to frame their comments sharing a little bit about fundamental questions, such as, "What is the research goal?" Not everybody stays up late at night hunched over a computer or a simulation or a robotic system, pondering the foundations of intelligence and human-level AI. We have here today Lise Getoor from the University ipate the liability and insurance industry; and the of Maryland; Devika Subramanian, who other one, that it was a human interface problem, comes to us from Rice University; we have Carlos that people don't necessarily want to go and type Guestrin from Carnegie Mellon University (CMU); a bunch of yes/no questions into a computer to get James Hendler from Rensselaer Polytechnic Institute an answer, even with a rule-based explanation, (RPI); Mike Wellman at the University of that if you'd taken that just a step further and Michigan; Henry Kautz at tjhe University of solved the human problem, it might have worked. Rochester; and Joe Konstan, who comes to us from Related to that, I was remembering a bunch of the Midwest, as our Minneapolis person here on these smart house projects. And I have to admit I the panel. I think everyone Joe Konstan: I was actually surprised when you hates smart spaces. I think of myself at the core there's nobody there, do you warn people and give in human-computer interaction. So I went back them a chance to answer? There's no good answer and started looking at what I knew of artificial to this question. I can tell you if that person is in intelligence to try to see where the path forward bed asleep, the answer is no, don't wake them up was, and I was inspired by the past.

Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in story-based games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences for actions. We implemented our RL technique in BioWare Corp.’s Neverwinter Nights. Our experiments evaluate an NPC companion’s behaviors regarding traps. Our method enables NPCs to rapidly learn reasonable behaviors and adapt to changes in the game.

Procedural content generation is becoming more and more relevant to solve the problem of content creation for the ever growing virtual worlds of games, simulations and other applications. However, these procedures are often unintuitive or use vague parameters, making it somewhat difficult for a designer to express his or her creative intent. Even worse, most of these techniques lack an accessible and easy to use interface.We have developed a generic layout solving approach to automatically create sensible content for virtual worlds. In that context, this paper proposes a high-level scene description language that allows designers to specify particular types of scenes. This description language allows designers to easily specify which objects need to be present in a scene, their attributes, and possible interrelationships. Application of the language, based on the rich vocabulary taken from a semantic library, is illustrated with several examples, showing its flexibility, intuitiveness and ease of use.

This research aims at building a preference-based player model of Civilization IV players. Our model incorporates attributes which are defined for AI players. We use a sequential minimal optimization (SMO) classifier to build the player model based on a training set with observations of a large number of games between six AI players. The model was validated on a test set of games between the same six AI players. While it did not seem to generalize well to the preferences of different AI players, it did manage to accurately predict some of the preferences for a veteran human player. Further tests showed that AI players with the same play styles but different preference values were often confused by the model. We conclude that for a complex game such as Civilization IV a model that attempts to accurately predict specific preference values is hard to construct. A model that focusses on play styles might succeed better.

The Pataphysic Institute (PI) is a research prototype multi-player game world. In PI, the personalities of the inhabitants are the base for the game mechanics. When interacting with other characters the potential emotional reactions depend upon avatars' current mood and personality. PI is built with inspiration from personality psychology and affect theory in an attempt to mimic possible emotional responses in order to give the player support in role-playing. The mental states of characters depend on their personalities and on their current moods. Moods differ according to context and to recent experiences. Emotional experiences become memories and define the relationships between characters. The mental state is the sum of the character and governs what actions can be performed in a given moment. In order to do certain things the characters need to be in certain moods — and for this the players need to game their avatars' emotions, and game their relationships.