As you may read from our first article farming robots are shaping agriculture and will feed humans of the future. Economics will demand a leap from theoretical concept of artificial intelligence to its practical application in agriculture, many experts suggest. But this process has already begun and is irreversible. Automated irrigation systems, crop health monitoring, face recognition systems for domestic cattle, CBR systems for fishing industry and many others are clear examples of how AI can be the Holy Grail for the farming industry. Irrigation systems are as old as man itself since agriculture is the foremost occupation of civilized humanity.

Reinforcement learning (RL) is a popular choice for solving robotic control problems. However, applying RL techniques to controlling humanoid robots with high degrees of freedom remains problematic due to the difficulty of acquiring sufficient training data. The problem is compounded by the fact that most real-world problems involve continuous states and actions. In order for RL to be scalable to these situations it is crucial that the algorithm be sample efficient. Model-based methods tend to be more data efficient than model-free approaches and have the added advantage that a single model can generalize to multiple control problems. This paper proposes a model approximation algorithm for continuous states and actions that integrates case-based reasoning (CBR) and Hidden Markov Models (HMM) to generalize from a small set of state instances. The paper demonstrates that the performance of the learned model is close to that of the system dynamics it approximates, where performance is measured in terms of sampling error.

This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.

Explanations play an important role in AI systems in general and case-based reasoning (CBR) in particular. They can be used for reasoning by the system itself or presented to the user to explain solutions proposed by the system. In our work we investigate the approach where causal explanations are automatically extracted from textual incident reports and reused in a CBR system for incident analysis. The focus of this paper is evaluation of such explanations. We propose an automatic evaluation measure based on the ability of explanations to provide an explicit connection between the problem description and the solution parts of a case.

We investigate case-based reasoning (CBR) problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. To this end, we employ abstract argumentation (AA) and propose a novel methodology for CBR, called AA-CBR. The argumentative formulation naturally allows to characterise the computation of an outcome as a dialogical process between a proponent and an opponent, and can also be used to extract explanations for why an outcome for a new case is (not) computed.

Roughly a year and a half ago, I had the privelage of taking a graduate "Introduction to Machine Learning" course under the tutelage of the fantastic Professor Leslie Kaelbling. While I learned a great deal over the course of the semester, there was one minor point that she made to the class which stuck with me more than I expected it to at the time: before using a really fancy or sophisticated or "in-vogue" machine learning algorithm to solve your problem, try a simple Nearest Neighbor Search first. Let's say I gave you a bunch of data points, each with a location in space and a value, and then asked you to predict the value of a new point in space. Perhaps the values of you data are binary (just s and -s) and you've heard of Support Vector Machines. Should you give that a shot?

Based on the theory of stochastic processes (Poisson processes) and the Erlang distribution, the estimated number of postings per time unit is indeed 2x the time since last posting. The theory will also give you the variance for this estimator (infinite) and will tell you that it's much more robust to use time to 2nd or 3rd or 4th previous posting, which have finite and known variances. Now if the group is inactive, the time to previous posting itself can be infinite, but in practice this is not an issue. Note that the Poisson assumption would be violated in this case. The theory will also suggest how to combine time to 2nd, time to 3rd and time to 4th previous posting to get a better estimator, read my paper Estimation of the Intensity of a Poisson process by means of neares... for details.

In this article, I compare two approaches (with their advantages and drawbacks) to compute a simple metric: the number of unique visitors ("uniques") per year for a website. I use the word user or visitor interchangeably. The problem seems straightforward at first glance, but it is not. It is a complex big data problem because the naive approach involves sorting hundreds of billions of observations - called transactions or page views here. It is also complicated because there's no 100% sure way to identify and track a user over long time periods: cookies and IP addresses / browser combinations both have drawbacks.

Higher-level cognition includes logical reasoning and the ability of question answering with common sense. The RatioLog project addresses the problem of rational reasoning in deep question answering by methods from automated deduction and cognitive computing. In a first phase, we combine techniques from information retrieval and machine learning to find appropriate answer candidates from the huge amount of text in the German version of the free encyclopedia "Wikipedia". In a second phase, an automated theorem prover tries to verify the answer candidates on the basis of their logical representations. In a third phase - because the knowledge may be incomplete and inconsistent -, we consider extensions of logical reasoning to improve the results. In this context, we work toward the application of techniques from human reasoning: We employ defeasible reasoning to compare the answers w.r.t. specificity, deontic logic, normative reasoning, and model construction. Moreover, we use integrated case-based reasoning and machine learning techniques on the basis of the semantic structure of the questions and answer candidates to learn giving the right answers.

In cooperation with the Association for the Advancement of Artificial Intelligence (AAAI), the Twenty-Second International Conference on Case-Based Reasoning (ICCBR), the premier international meeting on research and applications in case-based reasoning (CBR), was held from Monday September 29 to Wednesday October 1, 2014, in Cork, Ireland. ICCBR is the annual meeting of the CBR community and the leading conference on this topic. Started in 1993 as the European Conference on CBR and 1995 as ICCBR, the two conferences alternated biennially until their merger in 2010.