The challenge of using communications infrastructure to stabilize other infrastructures is related to research on the collective communications systems in social animals, robots, and human-non-human interaction. In these systems, voting models can explicate patterns of observed behavior or predict collective outcomes. Developing more theoretical deductive explanatory power can increase our knowledge about the interplay of voters and communication that produces collective inferences. This paper suggests that many analyses of voting patterns have not integrated what is known about the predictive properties of voting processes into their analyses. Taking a more deductive approach enables us to think about the strengths and weaknesses of existing explanations and imagine new types of analysis that have implications for engineering communications systems to stabilize other infrastructures.

The concept of modeling multiple complex adaptive systems (CAS) as if they were voting processes proposes that an Error Resilient Data Fusion (ERDF) method can help to mitigate the effects of emergent properties in CAS system-of-systems (SoS). The property of emergence in a CAS composed of multiple, multi-modal sensors poses specific problems for fusion processes due to the difficulty in predicting and accounting for sensor performance under disparate environmental conditions. This paper compares the voting and Choquet integral fusion methods in the context of a multi-modal sensor ERDF SoS.

It is now time to begin the study of personal genome services based on the interdisciplinary theories and technologies of genomics and artificial intelligence (AI). Although recently much attention has been given to personal genome services for realizing personal medicine, little systematic research has been done on their communication and computational aspects for intelligent wellness service in AI communities. We believe that the intelligent personal genome services of the future need to include an understanding of how the knowledge of genetic risk influences people's behavior. This paper proposes the concept of MyFinder, a new framework for realizing an intimate personal genome service with AI technologies. This paper also describes the grand challenge problems of personal genome services that the AI and genomics communities should tackle jointly.

If computational social science is to find practical application in informing policy decisions and proportionately analyzing courses of action, then it will have to make progress in the area of composition of social models.  Since a single simulation cannot hold a world of information, policy makers need to switch in and out modules in federations of simulations to test policies against all possible social environments.  Voting processes as they occur in nature, both in the form of cognition in a human mind of disparate world views, and in the form of equilibria seeking coevolution of species, inform how to combine model results externally and deeply, respectively.  These algorithms, which use the same principles of soft computation found in nature, enable any models to mesh together, even if they have different ontologies, or their data conflict, regardless of the degree they overlap.  A whiteboard architecture in which models report in their own ontologies how other models may inform them and what they have to offer other models, is a framework for the arbitrary meshing of social models.

The Jobs Puzzle, introduced in a book about automated reasoning, is a logic puzzle solvable by some "intelligent sixth graders," but the formalization of the puzzle by the authors was, according to them, "sometimes difficult and sometimes tedious." The puzzle thus presents a triple challenge: 1) formalize it in a non-difficult, non-tedious way; 2) formalize it in a way that adheres closely to the English statement of the puzzle; 3) have an automated general-purpose commonsense reasoner that can accept that formalization and solve the puzzle quickly. In this paper, I present and discuss three formalizations that are less difficult and less tedious than the original. However, none satisfy all three requirements as well as might be desired, and there are a significant number of automated reasoners that cannot solve the puzzle using any of the formalizations. So the Jobs Puzzle remains an interesting challenge.

Research in open-domain commonsense reasoning has been hindered by the lack of evaluation metrics for judging progress and comparing alternative approaches. Taking inspiration from large-scale question sets used in natural language processing research, we authored one thousand English-language questions that directly assess commonsense causal reasoning, called the Choice Of Plausible Alternatives (COPA) evaluation. Using a forced-choice format, each question gives a premise and two plausible causes or effects, where the correct choice is the alternative that is more plausible than the other. This paper describes the authoring methodology that we used to develop a validated question set with sufficient breadth to advance open-domain commonsense reasoning research. We discuss the design decisions made during the authoring process, and explain how these decisions will affect the design of high-scoring systems. We also present the performance of multiple baseline approaches that use statistical natural language processing techniques, establishing initial benchmarks for future systems.

Within the context of human/multi-robot teams, the "help me help you" paradigm offers different opportunities. A team of robots can help a human operator accomplish a goal, and a human operator can help a team of robots accomplish the same, or a different, goal. Two scenarios are examined here. First, a team of robots helps a human operator search a remote facility by recognizing objects of interest. Second, the human operator helps the robots improve their position (localization) information by providing quality control feedback.

Virtual and robotic agents must be able to understand "communicative acts" (utterances, gestures, controlled facial expressions etc.) if they are to interact and collaborate with humans. For researchers in AI, HCI, HRI and related fields, automatic comprehension of communicative acts has turned out to be a very tough nut to crack. Drawing on recent research from cognitive science and evolutionary psychology, the paper argues that an insufficient conceptualization of "context" is at the heart of this problem, and that we should focus on very simple, non-linguistic communicative acts (pointing gestures etc.) in order to investigate how agents can comprehend communicative acts in realistic contexts. I propose a tripartite model of context which is informed by experimental research on how humans recognize objects (via "affordances"), causal relations among objects, and the collaborative activities of fellow-humans. The model is not a formal one, but detailed enough to help in the development of comprehension algorithms in future research.

Humans naturally use multiple modes of instruction while teaching one another. We would like our robots and artificial agents to be instructed in the same way, rather than programmed. In this paper, we review prior work on human instruction of autonomous agents and present observations from two exploratory pilot studies and the results of a full study investigating how multiple instruction modes are used by humans. We describe our Bootstrapped Learning User Interface, a prototype multiinstruction interface informed by our human-user studies.

Stackelberg games have been widely used to model patrolling or monitoring problems in security. In a Stackelberg security game, the defender commits to a strategy and the adversary makes its decision with knowledge of the leader's commitment. Algorithms for computing the defender's optimal strategy are used in deployed decision-support tools in use by the Los Angeles International Airport (LAX), the Federal Air Marshals Service, and the Transportation Security Administration (TSA). Those algorithms take into account various resource usage constraints defined by human users. However, those constraints may lead to poor (even infeasible) solutions due to users' insufficient information and bounded rationality. A mixed-initiative approach, in which human users and software assistants (agents) collaborate to make security decisions, is needed. Efficient human-agent interaction process leads to models with higher overall solution quality. This paper preliminarily analyzes the needs and challenges for such a mixed-initiative approach.