In practical applications of robot swarms with bio-inspired behaviors, a human operator will need to exert control over the swarm to fulfill the mission objectives. In many operational settings, human operators are remotely located and the communication environment is harsh. Hence, there exists some latency in information (or control command) transfer between the human and the swarm. In this paper, we conduct experiments of human-swarm interaction to investigate the effects of communication latency on the performance of a human-swarm system in a swarm foraging task. We develop and investigate the concept of neglect benevolence, where a human operator allows the swarm to evolve on its own and stabilize before giving new commands. Our experimental results indicate that operators exploited neglect benevolence in different ways to develop successful strategies in the foraging task. Furthermore, we show experimentally that the use of a predictive display can help mitigate the adverse effects of communication latency.

Previous systems for literature based discovery, an automatic method of identifying hidden knowledge, have largely been based on bag of words approaches which perform only limited semantic analysis and interpretation. We describe the shortcomings of these approaches and suggest possible solutions that make use of techniques from Natural Language Processing.

The fable of Hansel and Gretel describes the plight of two children over two types of threat; harm to their immediate survival and pain from hunger. The two contexts of self-preservation and feeding are evident from the flow of the story dialog, therefore an automatic re-playing of dialog can be realized by picking sentences from two lists; one containing sentences in the context of self-preservation, the other in the context of feeding. Theory and Internet humor appreciation surveys suggest that humorous sentences in the context of self-preservation have relatively constant preference with respect to age, while in the context of hunger and protection of feeding turf to decline with age, reflecting the reduced need for food with aging. Sentences in the context of sociosexual relationships increased in preference until adulthood then declined with maturity. Also, sentences in parenting context, such as when caring for offspring, society and the environment were found to increase in preference with age and maturity. Therefore in order to construct a recursive Hansel and Gretel dialog for audience of all ages, two lists of sentences are added to feeding: In sociosexual and parenting contexts. The self-preservation list is paired with one of the remaining three, representing three stages of age; youth, adulthood and maturity. The single thread story of Hansel and Gretel serves as a template for recursive dialog, making it possible to create alternative threads and unbound possibilities for plots, thereby duplicating the story structure without repeating the narrative.

There is growing interest in automating the detection of interesting new developments in science and technology. BAE Systems is pursuing ARBITER (Abductive Reasoning Based on Indicators and Topics of EmeRgence), a multi-disciplinary study and development effort to analyze full- text and metadata for indicators of emergent technologies and scientific fields. To define these indicators, our team has applied the primary insights of actant network theory developed within the disciplines of Science and Technology Studies and the history of technology and science to create a pragmatic theory of technoscientific emergence. Specifically, this practical theory articulates emergence in terms of the robustness of actant networks. This applied actant-network theory currently guides our definition of indicators and indicator patterns for the ARBITER system, and represents a novel contribution to the discussion of emergent technologies and fields. Several elements of our theory were validated with 15 case studies and 25 example technologies.

We consider the problem of evaluating the performance of human contributors for tasks involving answering a series of questions, each of which has a single correct answer. The answers may not be known a priori. We assert that the measure of a contributor’s judgments is the amount by which having these judgments decreases the entropy of our discovering the answer. This quantity is the pointwise mutual information between the judgments and the answer. The expected value of this metric is the mutual information between the contributor and the answer prior, which can be computed using only the prior and the conditional probabil- ities of the contributor’s judgments given a correct answer, without knowing the answers themselves. We also propose using multivariable information measures, such as conditional mutual information, to measure the inter- actions between contributors’ judgments. These metrics have a variety of applications. They can be used as a basis for contributor performance evaluation and incentives. They can be used to measure the efficiency of the judgment collection process. If the collection process allows assignment of contributors to questions, they can also be used to optimize this scheduling.

List of organizers of the AAAI Fall Symposium on  Information Retrieval and Knowledge Discovery in Biomedical Text

Intelligent systems promise to amplify, augment, and extend innate human abilities. A principal example is that of assistive rehabilitation robots---artificial intelligence and machine learning enable new electromechanical systems that restore biological functions lost through injury or illness. In order for an intelligent machine to assist a human user, it must be possible for a human to communicate their intentions and preferences to their non-human counterpart. While there are a number of techniques that a human can use to direct a machine learning system, most research to date has focused on the contrasting strategies of instruction and reward. The primary contribution of our work is to demonstrate that the middle ground between instruction and reward is a fertile space for research and immediate technological progress. To support this idea, we introduce the setting of human-prompted switching, and illustrate the successful combination of switching with interactive learning using a concrete real-world example: human control of a multi-joint robot arm. We believe techniques that fall between the domains of instruction and reward are complementary to existing approaches, and will open up new lines of rapid progress for interactive human training of machine learning systems.

While machine learning methods have proven to be a highly valuable tool in solving numerous problems in assistive technology,state-of-the-art machine learning algorithms and corresponding results are not always accessible to assistive technology researchers due to required domain knowledge and complicated model parameters. This work explores the use of recent work in machine learning to entirely automate the machine learning pipeline, from feature extraction to classification. A nonparametrically guided autoencoder is used toextract features and perform classification while Bayesian optimization is used to automatically tune the parameters of the model for best performance. Empirical analysis is performed on a real-world rehabilitation research problem. The entirely automated approach significantly outperforms previously published results using carefully tuned machine learning algorithms on the same data.

We present an approach for enabling in-home service robots to follow natural language commands from non-expert users, with a particular focus on spatial language understanding. Specifically, we propose an extension to the semantic field model of spatial prepositions that enables the representation of dynamic spatial relations involving paths. The relevance of the proposed methodology to interactive robot learning is discussed, and the paper concludes with a description of how we plan to integrate and evaluate our proposed model with end-users.

In this paper, we investigate what role the network topology plays when controlling a network of mobile robots. This is a question of key importance in the emerging area of human-swarm interaction and we approach this question by letting a human user inject control signals at a single leader-node, which are then propagated throughout the network. Based on a user study, it is found that some topologies are more amenable to human control than others, which can be interpreted in terms of the rank of the controllability matrix of the underlying network dynamics, as well as, measures of node centrality on the leader of the network.