Multi-output Gaussian processes have received increasing attention during the last few years as a natural mechanism to extend the powerful flexibility of Gaussian processes to the setup of multiple output variables. The key point here is the ability to design kernel functions that allow exploiting the correlations between the outputs while fulfilling the positive definiteness requisite for the covariance function. Alternatives to construct these covariance functions are the linear model of coregionalization and process convolutions. Each of these methods demand the specification of the number of latent Gaussian process used to build the covariance function for the outputs. We propose in this paper, the use of an Indian Buffet process as a way to perform model selection over the number of latent Gaussian processes. This type of model is particularly important in the context of latent force models, where the latent forces are associated to physical quantities like protein profiles or latent forces in mechanical systems. We use variational inference to estimate posterior distributions over the variables involved, and show examples of the model performance over artificial data, a motion capture dataset, and a gene expression dataset.

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension. The VC dimension measures the capacity of a learning machine, and a smaller VC dimension leads to improved generalization. On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs. In this paper, we describe a neural network based on a linear dynamical system, that converges to the MCM solution. The proposed MCM dynamical system is conducive to an analogue circuit implementation on a chip or simulation using Ordinary Differential Equation (ODE) solvers. Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, as we obtain improved accuracies and fewer number of support vectors (upto 74.3% reduction) with the MCM dynamical system.

In order to properly regulate iterated belief revision, Darwiche and Pearl (1997) model belief revision as revising epistemic states by propositions. An epistemic state in their sense consists of a belief set and a set of conditional beliefs. Although the denotation of an epistemic state can be indirectly captured by a total preorder on the set of worlds, it is unclear how to directly capture the structure in terms of the beliefs and conditional beliefs it contains. In this paper, we first provide an axiomatic characterisation for epistemic states by using nine rules about beliefs and conditional beliefs, and then argue that the last two rules are too strong and should be eliminated for characterising the belief state of an agent. We call a structure which satisfies the first seven rules a general epistemic state (GEP). To provide a semantical characterisation of GEPs, we introduce a mathematical structure called belief algebra, which is in essence a certain binary relation defined on the power set of worlds.We then establish a 1-1 correspondence between GEPs and belief algebras, and show that total preorders on worlds are special cases of belief algebras. Furthermore, using the notion of belief algebras, we extend the classical iterated belief revision rules of Darwiche and Pearl to our setting of general epistemic states.

Recent advances in Symbolic Dynamic Programming (SDP) combined withthe extended algebraic decision diagram (XADD) have provided exactsolutions for expressive subclasses of finite-horizon Hybrid MarkovDecision Processes (HMDPs) with mixed continuous and discrete stateand action parameters. Unfortunately, SDP suffers from two majordrawbacks: (1) it solves for all states and can be intractable formany problems that inherently have large optimal XADD value functionrepresentations; and (2) it cannot maintain compact (pruned) XADDrepresentations for domains with nonlinear dynamics and reward due tothe need for nonlinear constraint checking. In this work, wesimultaneously address both of these problems by introducing real-timeSDP (RTSDP). RTSDP addresses (1) by focusing the solution and valuerepresentation only on regions reachable from a set of initial statesand RTSDP addresses (2) by using visited states as witnesses ofreachable regions to assist in pruning irrelevant or unreachable(nonlinear) regions of the value function. To this end, RTSDP enjoysprovable convergence over the set of initial states and substantialspace and time savings over SDP as we demonstrate in a variety of hybrid domains ranging from inventory to reservoir to traffic control.

Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.

Generalized Adaptive A* (GAA*) is an incremental algorithm that replans using A* when solving goal-directed navigation problems in dynamic terrain. Immediately after each A* search, it runs an efficient procedure that updates the heuristic values of states that were just expanded by A*, making them more informed. Those updates allow GAA* to speed up subsequent A* searches. Being based on A*, it is simple to describe and communicate; however, it is outperformed by other incremental algorithms like the state-of-the-art D*Lite algorithm at goal-directed navigation. In this paper we show how GAA* can be modified to exploit more information from a previous search in addition to the updated heuristic function. Specifically, we show how GAA* can be modified to utilize the paths found by a previous A* search. Our algorithm — Multipath Generalized Adaptive A* (MPGAA*) — has the same theoretical properties of GAA* and differs from it by only a few lines of pseudocode. Arguably, MPGAA* is simpler to understand than D*Lite. We evaluate MPGAA* over various realistic dynamic terrain settings, and observed that it generally outperforms the state-of-the-art algorithm D*Lite in scenarios resembling outdoor and indoor navigation.

Recent studies have suggested that current agent-based models are not sufficiently sophisticated to reproduce results achieved by human collaborative learning and reasoning. Such studies suggest that humans are diverse and dynamic when solving problems socially. However, despite their relevance to problem-solving, these two behavioral features have not yet been fully investigated. In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings. Specifically, we investigate the effects of separating exploitation from exploration in agent behaviors and explore the concept of diversity in such models. We found out that diverse populations outperform homogeneous ones in both efficient and inefficient networks. Finally, we show that agent diversity is more relevant than the strategic behavioral dynamics. This work contributes towards understanding the role of diverse and dynamic behaviors in social problem-solving as well as the advancement of state-of-art social problem-solving models.

Those of us involved In the creation of the Handbook of Artificial Intelligence, both writers and editors, have attempted to make the concepts, methods, tools, and main results of artificial Intelligence research accessible to a broad scientific and engineering audience. Currently, Al work Is familiar mainly to its practicing specialists and other interested computer scientists. Yet the field Is of growing interdisciplinary interest and practical Importance. With this book we are trying to build bridges that are easily crossed by engineers, scientists in other fields, and our own computer science colleagues. In the Handbook we Intend to cover the breadth and depth of Al, presenting general overviews of the scientific issues, as well as detailed discussions of particular to -hniques and Important Al systems.

There is little doubt that the decision to use rules to encode infectious disease knowledge in the nascent MYCIN system was largely influenced by our experience using similar techniques in DENDRAL. However, as mentioned in Chapter 1, we did experiment with a semantic network representation before turning to the production rule model. The impressive published examples of Carbonell's SCHOLAR system (Carbonell, 1970a; 1970b), with its ability to carry on a mixed-initiative dialogue regarding the geography of South America, seemed to us a useful model of the kind of rich interactive environment that would be needed for a system to advise physicians. Our disenchantment with a pure semantic network representation of the domain knowledge arose for several reasons as we began to work with Cohen and Axline, our collaborating experts. First, the knowledge of infectious disease therapy selection was ill-structured and, we found, difficult to represent using labeled arcs between nodes. Unlike South American geography, our domain did not have a clear-cut hierarchical organization, and we found it challenging to transfer a page or two from a medical textbook into a network of sufficient richness for our purposes. Of particular importance was our need for a strong inferential mechanism that would allow our system to reason about complex relationships among diverse concepts; there was no precedent for inferences on a semantic net that went beyond the direct, labeled relationships between nodes.1 Perhaps the greatest problem with a network representation, and the greatest appeal of production rules, was our gradually recognized need to deal with small chunks of domain knowledge in interacting with our expert collaborators.

Item response theory (IRT) models for categorical response data are widely used in the analysis of educational data, computerized adaptive testing, and psychological surveys. However, most IRT models rely on both the assumption that categories are strictly ordered and the assumption that this ordering is known a priori. These assumptions are impractical in many real-world scenarios, such as multiple-choice exams where the levels of incorrectness for the distractor categories are often unknown. While a number of results exist on IRT models for unordered categorical data, they tend to have restrictive modeling assumptions that lead to poor data fitting performance in practice. Furthermore, existing unordered categorical models have parameters that are difficult to interpret. In this work, we propose a novel methodology for unordered categorical IRT that we call SPRITE (short for stochastic polytomous response item model) that: (i) analyzes both ordered and unordered categories, (ii) offers interpretable outputs, and (iii) provides improved data fitting compared to existing models. We compare SPRITE to existing item response models and demonstrate its efficacy on both synthetic and real-world educational datasets.