This case study concerns the use of software agent advisors to improve efficiency and quality in issue tracking activities of development teams at the world's largest medical device manufacturer. Each software agent monitors, interacts with, and learns from its environment and user, recognizing when and how to provide different kinds of advice and support to facilitate issue tracking activities without directly modifying anything or otherwise violating domain constraints. The deployed software agent has not only enjoyed regular and growing use, but contributed to significant improvements. Issue rejection was significantly reduced and more focused, yielding significant quality and efficiency gains such as fewer reviews by quality assurance. This success reflects the benefits of the underlying AI technology.

Dots-And-Boxes is a well-known and widely-played combinatorial game. While the rules of play are very simple, the state space for even very small games is extremely large, and finding the outcome under optimal play is correspondingly hard. In this paper we introduce a Dots-And-Boxes solver which is significantly faster than the current state-of-the-art: over an order-of-magnitude faster on several large problems. Our approach uses Alpha-Beta search and applies a number of techniques---both problem-specific and general---that reduce the search space to a manageable size. Using these techniques, we have determined for the first time that Dots-And-Boxes on a board of 4 x 5 boxes is a tie given optimal play; this is the largest game solved to date.

In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Data and information from people, systems and things is the single most scalable resource available to city stakeholders but difficult to publish, organize, discover and consume, especially in a real-time context. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem, could drive significant benefits and opportunities. In the context of stream data (as real-time, gigantic, noisy and private data), this paper targets research issues we identify as important to harness the fused information resources of cities, Citizens and Stakeholders to reach the concept of Smarter Cities.

The aims of this workshop are to (1) Draw the attention of the AI community to the research challenges and opportunities in semantic cities. (2) Draw the attention on the multidisciplinary dimension and its impact on semantic cities such as transportation, energy, water management. (3) Identify unique issues of this domain and what new techniques may be needed. As example, since governments and citizens are involved data security and privacy are first-class concerns (4) Promoting more cities to become semantic cities (5) Elaborating a (semantic data) benchmark for testing AI techniques on semantic cities. (6) Provide a platform for sharing best-practices and discussion.

Refining a network topology is an important network management technique. Nevertheless, determining the appropriate steps to transform a network from one topology to another, in a way that minimizes service disruptions, has received little attention. This is a critical problem since service disruptions can be particularly harmful and costly for networks hosting mission-critical services. In this paper, we introduce the incremental network transformation (INT) problem and explore this problem in the context of automated planning. We define two metrics to measure the quality of generated transformation plans, one of which is amenable to classical propositional planning. We find that while state-of-the-art domain-independent planning techniques are effective at finding high-quality solutions for small problem instances, they cannot scale to solve realistically sized INT instances. To address the shortcomings of existing approaches, we developed a number of domain-dependent planners that use novel domain-specific heuristics. We empirically evaluated our planners on a wide range of synthetic network topologies. Our results illustrate that our automated planning inspired techniques are effective on realistically sized INT problems. We envision that our approach could eventually provide a compelling addition to the arsenal of techniques employed by network practitioners to support network refinement with minimal disruption to running services.

We present the domain-independent HRFF algorithm, which solves goal-oriented HMDPs by incrementally aggregating plans generated by the Metric-FF planner into a policy defined over discrete and continuous state variables. HRFF takes into account non-monotonic state variables, and complex combinations of many discrete and continuous probability distributions. We introduce new data structures and algorithmic paradigms to deal with continuous state spaces: hybrid hierarchical hash tables, domain determinization based on dynamic domain sampling or on static computation of probability distributions' modes, optimization settings under Metric-FF based on plan probability and length. We compare with HAO* on the Rover domain and show that HRFF outperforms HAO* by many order of magnitudes in terms of computation time and memory usage. We also experiment challenging and combinatorial HMDP versions of benchmarks from numeric classical planning, with continuous dead-ends and non-monotonic continuous state variables.

Goal-driven automated composition of software components is an important problem with applications in Web service composition and stream processing systems. The popular approach to address this problem is to build the composition automatically using Artificial Intelligence planning. However, it is shown that some of these popular planning approaches may neither be feasible nor scalable for many real large-scale flow-based applications. Recent advances have proven that the automated composition problem can take advantage of expert knowledge restricting the ways in which different reusable components can be composed. This knowledge can be represented using an extensible composition template or pattern. In prior work, a flow pattern language called Cascade and its corresponding specialized planner have shown the best performance in these domains. In this paper, we propose to address this problem using Hierarchical Task Network (HTN) planning. To this end, we propose an automated approach of creating an HTN-based problem from the Cascade representation of the flow patterns. The resulting technique not only allows us to use the HTN planning paradigm and its many advantages including added expressivity but also enables optimization and customization of composition with respect to preferences and constraints. Further, we propose and develop a lookahead heuristic and show that it significantly reduces the planning time. We have performed extensive experimentation in the context of the stream processing application and evaluated applicability and performance of our approach.

This paper presents a prototype system called SFplanner which uses an automated planning technique to generate workflows for reconfiguring a computing infrastructure. The system allows an administrator to specify a configuration task which consists of current state, desired state and global constraints. This task is compiled to a grounded finite-domain representation as the input for the standard (unmodified) Fast-Downward planner in order to automatically generate a workflow. The execution of the workflow will bring the system into the desired state, preserving the global constraints at every stage of the workflow.

Neurules are a type of neuro-symbolic rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Neurules exhibit characteristics such as modularity, naturalness and ability to perform interactive and integrated inferences. One way of producing a neurule base is through conversion of an existing symbolic rule base yielding an equivalent but more compact rule base. The conversion process merges symbolic rules having the same conclusion into one or more neurules. Due to the inability of the adaline unit to handle inseparability, more than one neurule for each conclusion may be produced. In this paper, we define criteria concerning the ability or inability to convert a rule set into a single neurule. Definition of criteria determining whether a set of symbolic rules can (or cannot) be converted into a single, equivalent but more compact rule is of general representational interest. With application of such criteria, the conversion process of symbolic rules into neurules becomes more time- and space-efficient by omitting useless trainings. Experimental results are promising.

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This paper describes a Neural-Symbolic Cognitive Agent that integrates neural learning, symbolic knowledge representation and temporal reasoning in a visual intelligent system that can reason about actions of entities observed in video. Results have shown that the system is able to learn and represent the underlying semantics of the actions from observation and use this for several visual intelligent tasks, like recognition, description, anomaly detection and gap-filling.