Since the inception of artificial intelligence, many have argued that abstraction, reformulation, and approximation (ARA) are central to human commonsense reasoning and problem solving and to the ability of computer systems to reason effectively in complex domains. The primary use of ARA techniques has been to overcome computational intractability by decreasing the combinatorial costs associated with searching large spaces. In addition, ARA techniques are useful for knowledge acquisition and explanation generation in complex domains. The International Symposium on Abstraction, Reformulation and Approximation (SARA) series was established in 1994 and continued in 1995, 1998, 2000, 2002, 2005, 2007, and 2009 to provide a way for researchers to share results on ARA. The Ninth International Symposium on Abstraction, Reformulation and Approximation was held July 17-18, 2011 at a renovated medieval castle in the Parador de Cardona hotel in Cardona, Catalonia, Spain, about 60 miles northwest of Barcelona.

Skeptics tend not to be the first to jump on the next band- wagon. In quite a few areas of science, simulations and Com- plex Adaptive Systems (CAS) has been the bandwagon in question. This paper intends to reach out to the skeptics and convince them to hop-on; take over the controls and make the wagon do a U-turn and aim for the established scientific theories. The argument is that simulation techniques, such as Agent- Based Modelling (ABM), may possibly be epistemically problematic as one sets out to strongly corroborate theories concerned with our overly complex real world. However, us- ing the same techniques to explore the robustness of (or to falsify) existing abstract and idealised mathematical models will be to be epistemically uncomplicated. This allows us to study the effects of reintroduction of real-world traits, such as autonomy and heterogeneity that was previously sacrificed for mathematical tractability.

Cognitive systems with human-level intelligence must dis­play a wide range of abilities, including reasoning about the beliefs of others, hypothetical and future situations, quanti­fiers, probabilities, and counterfactuals. While each of these deals in some way with reasoning about alternative states of reality, no single knowledge representation framework deals with them in a unified and scalable manner. As a conse­quence it is difficult to build cognitive systems for domains that require each of these abilities to be used together. To enable this integration we propose a representational framework based on synchronizing beliefs between worlds. Using this framework, each of these tasks can be reformu­lated into a reasoning problem involving worlds. This demonstrates that the notions of worlds and inheritance can bring significant parsimony and broad new abilities to knowledge representation.

Security operators are increasingly interested in solutions that can provide an automatic understanding of potentially crowded public environments. In this paper, an on-going research is presented, on building a complex system consists of three main components: human security operators carrying sensors, mobile robotic platforms carrying sensors and network of fixed sensors (i.e. cameras) installed in the environment. The main objectives of this research are: 1) to develop models and solutions for an intelligent integration of sensorial information coming from different sources, 2) to develop effective human-robot interaction methods in the paradigm multi-human vs. multi-robot, 3) to integrate all these components in a system that allows for robust and efficient coordination among robots, vision sensors and human guards, in order to enhance surveillance in crowded public environments.

Millions of people globally have been forcibly displaced from their homes due to reasons beyond their control such as conflict, political upheaval, and environmental catastrophes. In many cases, these forced migrants seek temporary refuge in camps managed by nongovernmental organizations (NGOs). Although responsibility for refugees’ well-being within camps belongs mainly to the NGOs and host government, the density of the camp population and lack of resources of service providers leads to a high degree of insecurity. Building off successful models of mHealth, or utilizing mobile technologies to address healthcare needs, this paper explores the possibility of using communication technologies to address personal security issues. Using agent based modeling techniques, this paper examines the ways in which information about incidents of violence are communicated through a closed population. In this way, the authors advocate for the use of mobile phones in an mSecurity context that empowers forced migrants to become active members in reducing incidents of violence within refugee and internally displaced persons camps.

This paper describes a prototype system that generates mathematical word problems from ontologies in unrestricted domains. It builds on an existing ontology verbaliser that renders logical statements written in Web Ontology Language (OWL) as English sentences. This kind of question is more complex than those normally attempted by question generation systems, since mathematical word problems consist of a number of sentences that communicate a short narrative (in addition to providing the relevant numerical information required to solve the underlying mathematical problem). Thus, they embody many research issues that do not crop up with single-sentence questions. As well as describing the prototype system, I discuss five ways in which the difficulty of the generated questions may be controlled automatically during generation.

Presumably any human-level cognitive system (HLCS) must have the capacity to: maintain and learn new concepts; believe propositions about its environment that are constructed from these concepts, and from what it perceives; reason over the propositions it believes, in order to among other things manipulate its environment and justify its significant decisions; and learn new concepts. Given this list of desiderata, it’s hard to see how any intelligent attempt to build or simulate a HLCS can avoid falling under a neo-Piagetian approach to engineering HLCSs. Unfortunately, such engineering has been discursively declared by Jerry Fodor to be flat-out impossible. After setting out Fodor’s challenges, we refute them and, inspired by those refutations, sketch our solutions on behalf of those wanting to computationally model and construct HLCSs, under neo-Piagetian assumptions.

In this paper we develop a model to simulate the evolution of a pollution-free resource gathering technology that is initially less efficient but ultimately reaches parity with polluting technology. We find that for low levels of pollution, pressure exerted by society can indeed encourage the development and use of non-polluting technology, with greater pressure being associated with faster achievement of efficiency parity and lower overall pollution. However, greater pressure is also associated with lower populations and at the highest levels of pressure there are significant risks of population crashes. We find that these results hold for both localized pollution and globalized pollution, with globalized pollution encouraging faster achievement of efficiency parity. For high levels of pollution we find that introducing societal pressure significantly increases the occurrence of population crashes, and thus the strategy is only effective under certain conditions.

Question ambiguity is one major factor that affects question quality. Less ambiguous questions can be produced by using more specific question words. We attack the problem of how to ask more specific questions by supplementing question words with the hypernyms for answer phrases. This dramatically increases the coverage of generated "which" questions. Evaluation results show improved question quality when the question words are disambiguated correctly given the context.

We present a graph-theoretic model of discourse based on the Questions Under Discussion (QUD) framework. Questions and assertions are treated as edges connecting discourse states in a rooted graph, modeling the introduction and resolution of various QUDs as paths through this graph. The amount of common ground presupposed by interlocutors at any given point in a discourse corresponds to graphical depth. We introduce a new task-oriented dialogue corpus and show that experts, presuming a richer common ground, initiate discourse at a deeper level than novices. The QUD-graph model thus enables us to quantify the experthood of a speaker relative to a fixed domain and to characterize the ways in which rich common ground facilitates more efficient communication.