Individuals with high-functioning autism spectrum disorders (HFASD) have very individualistic needs, abilities, and are surrounded by very different social contexts. Consequently, special education and therapeutic interventions often need to be adapted to a particular individual. We are interested in developing systems that can help adolescents with HFASD rehearse and learn social skills with reduced aide from parents, guardians, teachers, and therapists. We describe a social skill learning game that utilizes social scenarios. Because of the individualistic needs and abilities of our target users, we describe ongoing work on AI to assist caregivers with the authoring of tailored social scenarios.

This paper describes some functionalities and features of the Maryland Virtual Patient (MVP) environment. MVP models the process of disease progression, diagnosis and treatment in virtual patients who are endowed with a “body,” a simulation of their physiological and pathological processes, and a “mind,” a set of capabilities of perception, reasoning and action that allow the virtual patient to exhibit independent behavior, participate in a natural language dialog, remember events, hold beliefs about other agents and about specific object and event instances, make decisions and learn.

Defeasible Logic Programming (DELP) is a formalism that extends declarative programming to capture defeasible reasoning. Its inference mechanism, upon a query on a literal in a program, answers by indicating whether or not it is warranted in an argumentation process. While the properties of DELP are well known, some of its basic elements can be redefined in order to shed light on some of the subtleties of the warrant process. We will discuss these alternative definitions and the cases in which they provide a better performance.

Assumption-Based Argumentation (ABA), and to a large extent argumentation in general, up to now has been considered in a single-agent setting. ABA, in particular, is such that an agent engages in a dispute (dialectic proof procedure) with itself (an imaginary opponent) to decide whether a claim is acceptable according to some acceptability criteria. We present in this paper a generalised proof procedure for the admissibility semantics of ABA, which is still a dispute by an agent with itself but such that the outcome can be readily communicated to other agents. This is important for applications in multi-agent systems wherein agents may differ in the knowledge they have and may need to communicate their arguments between one another to convince each other of the acceptability or not of a given claim.

Argumentation is a form of reasoning that makes explicit the reasons for the conclusions that are drawn and how con- flicts between reasons are resolved. This provides a natural mechanism, for example, to handle inconsistent and uncer- tain information and to resolve conflicts of opinion between intelligent agents. The advantage of a mechanism based on argumentation is that considering the reasons behind the conclusions offers more than considering the conclusions alone (to adapt something Isaac Bashevis Singer once said, the approach has “more vitamins” than other approaches to reasoning). For example, in dealing with inconsistent infor- mation, an early use of argumentation, it is possible to know more than just that we have the inconsistent conclusions p and not p. We can establish exactly which pieces of infor- mation lead to these conclusions and can then prioritize one conclusion over another on the basis of this information, de- cide what information should be revised to achieve consis- tency, or even determine what additional investigation needs to be carried out (when we have reason to believe both that it is raining outside and not raining outside, and have no way of determining which is correct, going to look may be the best solution).

Multi-representational architectures exploit diversity to yield the breadth of capabilities required for intelligent behavior in the world, but in so doing can sacrifice too much of the complementary benefits of architectural uniformity. The proposal here is to couple the benefits of diversity and uniformity through establishment of a uniform graph-based implementation level for diverse architectures.

PMRFSim is a proof of concept geospatial social agent-based simulation capable of examining the interactions of 60,000+ agents over a simulated year within a few minutes. PMRFSim utilizes real world data from sources ranging from the U.S. Census Bureau, a regional sociologist, and base security. PMRFSim models two types of agents, normal and adverse agents. Adverse agents have harmful intent and goals to spread negative sentiment and acquire intelligence. All agents are endowed with demographic and geospatial attributes. Agents interact with each other and respond to events. PMRFSim allows an analyst to construct various what-if scenarios and generates numerous graphs that characterize the social landscape. This analysis is intended to aid public affairs officers understand the social landscape.

Boid flocking is a system in which several individual agents follow three simple rules to generate swarm-level flocking behavior. To control this system, the user must adjust the agent program parameters, which indirectly modifies the flocking behavior. This is unintuitive because the properties of the flocking behavior are non-explicit in the agent program. In this paper, we discuss a domain-independent approach for detecting and controlling two emergent properties of boids: density and a qualitative threshold effect of swarming vs. flocking. Also, we discuss the possibility of applying this approach to detecting and controlling traffic jams in traffic simulations.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. The CS may be viewed as a complex adaptive system, in which individual components are linked through multiple feedback and feedforward loops. The non-linear relationships between the numerous coagulation factors and the interplay among the elements of the CS render the study of this biology at a molecular and cellular level nearly impossible. We present an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 52 rules to define the interactions among 33 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~12,000 cells and ~300,000 agents. Our ABMS method successfully reproduces the initiation, propagation, and termination of thrombin formation due to the activation of the extrinsic pathway. Furthermore, the ABMS is able to demonstrate the emergence of a threshold effect for thrombin generation as a result of the synergistic effect of combining anticoagulant systems.

Irregular Warfare (IW), with its emphasis on social and cognitive phenomena such as population sentiment, is a major new focus of the Department of Defense (DoD). One of the most important classes of IW action is Information Operations (IO), the use of information to influence sentiment. With the DoD’s new focus on IW comes the new need to analyze and forecast the effects of IO actions on population sentiment. Analysts at the DoD traditionally use Modeling and Simulation to analyze and forecast the effects of conventional warfare’s actions on the outcome of wars, but IW and IO in particular are far more complex than conventional physics-based simulations. DoD analysts are in the early stages of looking for scientifically rigorous methods in the Modeling and Simulation of IO’s complex effects. This paper presents the state of IO modeling and simulation in the DoD, using examples from several computer models now being used, in these early stages of IW analysis. It discusses how the ideas of Complex Adaptive Systems (CAS) and threshold events in particular may be incorporated into IO modeling in order to increase its scientific rigor, fidelity, and validity.