A form of categorical perception occurs constantly outside the laboratory, as when different The history of research on speech perception is speakers produce the "same" word or when a speaker says replete with examples of nonlinearities, or threshold the "same" word quickly or slowly. This means that phenomena, relating acoustics to perception. These speech perception cannot be a simple concatenation of nonlinearities are essential in that they allow stable sound elements to yield syllables, syllables to yield communication despite variation in the acoustic signal words, or words to yield sentences. The interdependency across speakers, emphasis, background noise, etc. across scales reveals a complex system with nonlinearly Furthermore, the range of acoustic signals perceived as interacting elements that somehow allow veridical equivalent is much larger for speech sounds than for communication.

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.

This paper argues that supervised cognitive growth in artifacts will be very difficult to achieve without detailed knowledge about systems’ internal states. Physical information is too low level to provide a useful understanding of a system’s behavior, and it is more pragmatically useful to take a mental stance towards an artificial system and interpret its actions in terms of mental states. This mental stance is similar to Dennett’s intentional stance, except the ascription of beliefs and rationality in the intentional stance is replaced by the attribution of low level mental states in the mental stance. In some cases it might also be useful to take a conscious stance towards an artificial system that interprets its behavior as the outcome of a conscious decision making process. Since most artifacts lack language, automatic analysis techniques have to be used to identify the contents of their minds, and the second half of this paper suggests how some of the earlier work of Aleksander and Atlas can be applied in this area.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

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.

The objective of our research is to facilitate social conversation between persons affected with Alzheimer’s Disease (AD) and their caregivers via a future intervention for caregivers. In the intervention, a computer system will enable caregivers to practice spoken conversation with high-fidelity Virtual Patients simulating the verbal and non-verbal behavior of persons with AD (VP-AD). It is hoped that the skills acquired by the caregiver will improve the quality of life of persons with AD and reduce caregiver stress. In this paper, we describe a pilot study intended to evaluate the efficacy of active participation in conversation with a lower fidelity VP-AD in comparison to passive observation of the same VP-AD in conversation. The study found, after 15 minutes or less of practice, a significant increase in use of recommended caregiver communication skills by participants in the active condition.

By this paper we would like to open a discussion on the need ofBy this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter.

Thought is an essential aspect of mental function, but remains very poorly understood. In this paper, we take the view that thought is a response process — the emergent and dynamic configuration of structured response, i.e., ideas, by composing response elements, i.e., concepts, from a repertoire under the influence of afferent information, internal modulation and evaluative feedback. We hypothesize that the process of generating ideas occurs at two levels: 1) The identification of a context-specific subset — or workspace — of concepts from the larger repertoire; and 2) The configuration of plausible/useful ideas within this workspace. Workspace configuration is mediated by a dynamic selector network (DSN), which is an internal attention/working memory system. Each unit of the DSN selectively gates a subset of concepts, so that any pattern of activity in the DSN defines a workspace. The configuration of efficient and flexible workspaces is mediated by dynamical structures termed graded attractors — attractors where the set of active units can be varied in systematic order by inhibitory modulation. A graded attractor in the DSN can project a selective bias — a ``searchlight" — onto the concept repertoire to define a specific workspace, and inhibitory modulation can be used to vary the breadth of this workspace. As it experiences various contexts, the cognitive system can configure a set of graded attractors, each covering a domain of similar contexts. In this paper, we focus on a mechanism for configuring context-specific graded attractors, and evaluate its performance over a set of contexts with varying degrees of similarity. In particular, we look at whether contexts are clustered appropriately into a minimal number of workspaces based on the similarity of the responses they require. While the focus in this paper is on semantic workspaces, the model is broadly applicable to other cognitive response functions such as motor control or memory recall.

Uncertainty in natural language dialogue is often treated through stochastic models. Some of the authors already presented TutorJ that is an Intelligent Tutoring System, whose interaction with the user is very intensive, and makes use of both dialogic and graphical modality. When managing the interaction, the system needs to cope with uncertainty due to the understanding of the user's needs and wishes. In this paper we present the extended version of TutorJ, focusing on the new features added to its chatbot module. These features allow to merge deterministic and probabilistic reasoning in dialogue management, and in writing the rules of the system's procedural memory.

We argue that transfer of spatial and conceptual knowledge between tasks and domains is an essential benchmark for multi-representational architectures aimed at human-level intelligence. The underlying hypothesis is that spatial relationships provide a natural level of abstraction, highlighting the similarities and differences between situations and domains. Therefore, not only will spatial representations improve domain reasoning and learning, they will also facilitate the transfer of knowledge across domains. The simulated environments of real-time strategy (RTS) games provide an excellent test-bed for exploring this hypothesis for two reasons: many different RTS domains have been constructed and RTS requires a wide range of reasoning tasks.