TOWARD A PLATFORM FOR MULTI-LAYERED MULTI-AGENT SITUATED SYSTEM (MMASS)-BASED SIMULATIONS: FOCUSING ON FIELD DIFFUSION

The paper introduces some issues and related solutions adopted in order to realize the MMASS platform. This is a framework to specify and execute simulation applications based on the multilayered multi-agent situated system model (MMASS). MMASS is a model for multi-agent systems (MAS) situated in an environment whose structure is explicitly defined. The behavior and interaction models of MMASS agents are strongly related to the spatial structure of their environment. The MMASS model is the result of a long-term research that has its roots and motivations on application domains and problems that require spatial features to be considered. Our experiences with these problems have concerned the design of domain models and their implementations, according to the MAS approach for simulation purposes. This activity has revealed that currently available tools do not support the management of spatial features of agent environment and interaction mechanisms defined by the MMASS model and thus they are not suitable for our purposes. The paper focuses on the MMASS platform that aims to support the specification and development of applications (mainly, simulations) based on MMASS. Design issues and related solutions that have been adopted in order to manage those aspects that characterize the MMASS model will be shown. After a description of the conceptual model that underlies the MMASS platform and its general architecture, we will overview how the platform supports the specification of agent structured environment, behavior and interaction, and how it supports the execution of agent actions and interactions. Then we will describe issues and adopted solutions (both algorithmic and implementative ones) to manage at-a-distance interaction among MMASS agents

From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.

Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities. This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd

Towards an Indexical Model of Situated Language Comprehension for Cognitive Agents in Physical Worlds

We propose a computational model of situated language comprehension based on the Indexical Hypothesis that generates meaning representations by translating amodal linguistic symbols to modal representations of beliefs, knowledge, and experience external to the linguistic system. This Indexical Model incorporates multiple information sources, including perceptions, domain knowledge, and short-term and long-term experiences during comprehension. We show that exploiting diverse information sources can alleviate ambiguities that arise from contextual use of underspecific referring expressions and unexpressed argument alternations of verbs. The model is being used to support linguistic interactions in Rosie, an agent implemented in Soar that learns from instruction.Comment: Advances in Cognitive Systems 3 (2014

The Contribution of Society to the Construction of Individual Intelligence

It is argued that society is a crucial factor in the construction of individual intelligence. In other words that it is important that intelligence is socially situated in an analogous way to the physical situation of robots. Evidence that this may be the case is taken from developmental linguistics, the social intelligence hypothesis, the complexity of society, the need for self-reflection and autism. The consequences for the development of artificial social agents is briefly considered. Finally some challenges for research into socially situated intelligence are highlighted

A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations

This article proposes a methodology to model and simulate complex systems, based on IRM4MLS, a generic agent-based meta-model able to deal with multi-level systems. This methodology permits the engineering of dynamic multi-level agent-based models, to represent complex systems over several scales and domains of interest. Its goal is to simulate a phenomenon using dynamically the lightest representation to save computer resources without loss of information. This methodology is based on two mechanisms: (1) the activation or deactivation of agents representing different domain parts of the same phenomenon and (2) the aggregation or disaggregation of agents representing the same phenomenon at different scales.Comment: Presented at 3th International Workshop on Multi-Agent Based Simulation, Valencia, Spain, 5th June 201

Flexibly Instructable Agents

This paper presents an approach to learning from situated, interactive tutorial instruction within an ongoing agent. Tutorial instruction is a flexible (and thus powerful) paradigm for teaching tasks because it allows an instructor to communicate whatever types of knowledge an agent might need in whatever situations might arise. To support this flexibility, however, the agent must be able to learn multiple kinds of knowledge from a broad range of instructional interactions. Our approach, called situated explanation, achieves such learning through a combination of analytic and inductive techniques. It combines a form of explanation-based learning that is situated for each instruction with a full suite of contextually guided responses to incomplete explanations. The approach is implemented in an agent called Instructo-Soar that learns hierarchies of new tasks and other domain knowledge from interactive natural language instructions. Instructo-Soar meets three key requirements of flexible instructability that distinguish it from previous systems: (1) it can take known or unknown commands at any instruction point; (2) it can handle instructions that apply to either its current situation or to a hypothetical situation specified in language (as in, for instance, conditional instructions); and (3) it can learn, from instructions, each class of knowledge it uses to perform tasks.Comment: See http://www.jair.org/ for any accompanying file

Interactive situation modelling in knowledge intensive domains

Interactive Situation Modelling (ISM) method, a semi-methodological approach, is proposed to tackle issues associated with modelling complex knowledge intensive domains, which cannot be easily modelled using traditional approaches. This paper presents the background and implementation of ISM within a complex domain, where synthesizing knowledge from various sources is critical, and is based on the principles of ethnography within a constructivist framework. Although the motivation for the reported work comes from the application presented in the paper, the actual scope of the paper covers a wide range of issues related to modelling complex systems. The author firstly reviews approaches used for modelling knowledge intensive domains, preceded by a brief discussion about two main issues: symmetry of ignorance and system behaviour, which are often confronted when applying modelling approaches to business domains. The ISM process is then characterized and critiqued with lessons from an exemplar presented to illustrate its effectiveness

Embodied cognition: A field guide

The nature of cognition is being re-considered. Instead of emphasizing formal operations on abstract symbols, the new approach foregrounds the fact that cognition is, rather, a situated activity, and suggests that thinking beings ought therefore be considered first and foremost as acting beings. The essay reviews recent work in Embodied Cognition, provides a concise guide to its principles, attitudes and goals, and identifies the physical grounding project as its central research focus

Sensorimotor coordination and metastability in a situated HKB model

Oscillatory phenomena are ubiquitous in nature and have become particularly relevant for the study of brain and behaviour. One of the simplest, yet explanatorily powerful, models of oscillatory Coordination Dynamics is the Haken–Kelso–Bunz (HKB) model. The metastable regime described by the HKB equation has been hypothesised to be the signature of brain oscillatory dynamics underlying sensorimotor coordination. Despite evidence supporting such a hypothesis, to our knowledge, there are still very few models (if any) where the HKB equation generates spatially situated behaviour and, at the same time, has its dynamics modulated by the behaviour it generates (by means of the sensory feedback resulting from body movement). This work presents a computational model where the HKB equation controls an agent performing a simple gradient climbing task and shows (i) how different metastable dynamical patterns in the HKB equation are generated and sustained by the continuous interaction between the agent and its environment; and (ii) how the emergence of functional metastable patterns in the HKB equation – i.e. patterns that generate gradient climbing behaviour – depends not only on the structure of the agent's sensory input but also on the coordinated coupling of the agent's motor–sensory dynamics. This work contributes to Kelso's theoretical framework and also to the understanding of neural oscillations and sensorimotor coordination