BLOG is a powerful language to express models with an unknown number of objects and identity uncertainty. Current inference engines for BLOG are either too slow or require users to write a model-specific proposal distribution. We describe here, ongoing work to design a new, fast, generic inference engine for BLOG called blogc. The new implementation uses Gibbs sampling for finite-valued variables and performs an analysis of the model to generate customized sampling code in C. We describe our algorithms and methods in the context of various commonly used models and demonstrate significant performance improvement.

In this work, we present maximum causal entropy correlated equilibria, a new solution concept that we apply to Markov games. This contribution extends the existing solution concept of maximum entropy correlated equilibria for normal-form games to settings with elements of dynamic interaction with a stochastic environment by employing the recently developed principle of maximum causal entropy. This solution concept is justified for two purposes: as a mechanism for prescribing actions, it reveals the least additional information about the agents' motives possible; and as a predictive estimator of actions for a group of agents assumed to behave according to an unknown correlated equilibrium, it has the fewest additional assumptions and minimizes worst-case action prediction log-loss. Importantly, equilibria for this solution concept are guaranteed to be unique and Markovian, enabling efficient algorithms for finding them.

Knowledge compilation is a valuable tool for dealing with the computational intractability of propositional reasoning. In knowledge compilation, a representation in a source language is typically compiled into a target language in order to perform some reasoning task in polynomial time. One particularly popular target language is Deterministic Decomposable Negation Normal Form (d-DNNF). d-DNNF supports efficient reasoning for tasks such as consistency checking and model counting, and as such it has proven a useful representation language for Bayesian inference, conformant planning, and diagnosis. In this paper, we exploit recent advances in #SAT solving in order to produce a new state-of-the-art CNF → d-DNNF compiler. We evaluate the properties and performance of our compiler relative to C2D, the de facto standard for compiling to d-DNNF. Empirical results demonstrate that our compiler is generally one order of magnitude faster than C2D on typical benchmark problems while yielding a d-DNNF representation of comparable size.

Learning useful information when solving SAT or CSP problems to speed up a tree-search approaches, is one of the main explored tracks in various works. Such information are known as goods and nogoods and they aim to forbid to repetitively visit the same parts of the search space. Unfortunately and unlike nogoods, the exploitation of goods is limited to tree-search approaches based on the structural properties of the problem. In this paper, we propose to generalize and reformulate structural goods under SAT. We also propose a learning scheme of general goods and show their integration in a DPLL-like procedure.

Constraint satisfaction problems are mathematical models of real-world problems. In contrast to randomly generated artificial problems, real-world problems usually have non-random structure. Knowledge about that structure, when identified in advance, can make search to find solutions more effective. This paper introduces DrawCSP, a visualization program that can show both the original and the discovered structure of constraint satisfaction problems. DrawCSP provides insight into both search algorithm design and into the challenges real-world problems present.

Early detection of cognitive impairment can prevent or delay the progress of cognitive dysfunction. In the field of neurology, the Clock Drawing Test (CDT) is one of the most popular instruments for detecting cognitive impairment. This paper presents the development of the ClockReader system, a computerized Clock Drawing Test. The main function of the system is to automate error handling in handwriting recognition. Since the ClockReader is a screening tool for dementia, it is not desirable to ask the users to fix their input errors in the drawing of either numbers or characters. Therefore, we propose a simple machine learning technique, context-bounded refinement filter algorithm. With trial experiments, we prove that this simple algorithm improves the recognizer accuracy of handwriting in clock drawings up to 88%.

Based on a typology of five basic forms of abduction, I propose a new definition of abductive insight that empha sizes in particular the inferential structure of a belief system that is able to explain a phenomenon after a new, abductive ly created component has been added to this system or the entire system has been abductively restructured. My thesis is, first, that the argumentative structure of the pursued problem solution guides abductive creativity and, second, that diagrammatic reasoning—if conceptualized according to the requirements defined by Charles Peirce—can support this guidance. This support is mainly possible based on the normative power of the system of representation that has to be used to construct diagrams and to perform experiments with them.

Tactile maps offer access to spatial-analog information for visually impaired people. In contrast to visual maps, a tactile map has a lower resolution and can only be inspected in a sequential way, complicating the extraction of spatial relations among distant map entities. Verbal assistance can help to overcome these difficulties by substituting textual labels with verbal descriptions and offering propositional knowledge about spatial relations. Like visual maps, tactile maps are based on visual, spatial-geometric representations that need to be reasoned about in order to generate verbal assistance. We present an approach towards a verbally assisting virtual-environment tactile map (VAVETaM) realized on a computer system utilizing a haptic force-feedback device. In particular, we discuss the tasks of understanding the user's map exploration procedures (MEPs), of exploiting the spatial-analog map to anticipate the user's informational needs, of reasoning about optimal assistance by taking assumed prior knowledge of the user into account, and of generating appropriate verbal instructions and descriptions to augment the map.

Providing energy conservation services becomes a hot research topic because more and more people attach importance to environmental protection. This research proposes a framework that consists of four process models: appliance recognition, activity-appliances model, unattended appliances detection, and energy conservation service. Appliance recognition model can recognizes the operating states of appliances from raw sensing data of electric power. An activity-appliances model has been built to associate activities with appliances according to the data of Open Mind Common Sense Project. Using the relationship between activities can help to detect unattended appliances, which are consuming electric power but not take part in the resident’s activities. After obtain information of appliance operating states and unattended appliances, residents can receive energy conservation services for notifying the energy consumption information. Finally, the experimental results show that dynamic Baysian network approach can achieve higher than 92% accuracy for appliance recognition. Data of activity-appliances model shows most appliances are strong activity-related.

Traditionally, in the fields of artificial intelligence and robotics, representations of the self have been conspicuously absent. Capabilities of systems are listed explicitly by developers during construction and choices between behavioral options are decided based on search, inference, and planning. In robotics, while knowledge of the external world has often been acquired through experience, knowledge about the robot itself has generally been built in by the designer. Built-in models of the robot's kinematics, physical and sensory capabilities, and other equipment have stood in the place of self-knowledge, but none of these representations offer the flexibility, robustness, and functionality that are present in people. In this work, we seek to emulate forms of self-awareness developed during human infancy in our humanoid robot, Nico. In particular, we are interested in the ability to reason about the robot's embodiment and physical capabilities, with the robot building a model of itself through its experiences.