Spatio-temporal reasoning plays a significant role in situation management that is performed by intelligent agents
(human or machine) by affecting how the situations are recognized, interpreted, acted upon or predicted. Many
definitions and formalisms for the notion of spatio-temporal reasoning have emerged in various research fields
including psychology, economics and computer science (computational linguistics, data management, control
theory, artificial intelligence and others). In this paper we examine the role of spatio-temporal reasoning in situation
management, particularly how to resolve situations that are described by using spatio-temporal relations among
events and situations. We discuss a model for describing context sensitive temporal relations and show have the
model can be extended for spatial relations.
Context plays a significant role in situation resolution by intelligent agents (human or machine) by affecting how the
situations are recognized, interpreted, acted upon or predicted. Many definitions and formalisms for the notion of context
have emerged in various research fields including psychology, economics and computer science (computational
linguistics, data management, control theory, artificial intelligence and others). In this paper we examine the role of
context in situation management, particularly how to resolve situations that are described by using fuzzy (inexact)
relations among their components. We propose a language for describing context sensitive inexact constraints and an
algorithm for interpreting relations using inexact (fuzzy) computations.
The response, rescue and recovery teams that are engaged in disaster management operations require a continuous and
comprehensive information flow of the disaster environment and a situational awareness in order to undertake fast and
coordinated actions. Because of highly dynamic and often unpredictable disaster situations the teams need to adjust their
goals, resources and actions both on an individual member level (agent) and on an entire team level (multi-agent
system). This paper investigates a new approach to an agent's adaptability based on cognitive feedback introduced into
the framework of inter-agent collaboration. The paper is a continuation of our work on situation-aware multi-agent
systems. We discuss how agent adaptation and cognitive feedback is applied in the architecture of multi-agent systems
for disaster situation management.
Forces engaged in tactical urban combat operations require a continuous and comprehensive picture of the combat
environment to quickly detect the target and effectively reduce opposition. This requires a significant coordination of
actions, situational awareness, and fast decision-making. This paper focuses on modeling the collaboration acts among
multiple combat units. We analyze the tactics of urban combat operations and then propose a general framework for
modeling collaboration among units. Our approach uses Multi-Agent Systems (MAS) as a paradigm for modeling
Command and Control (C2) in urban combat operations. We introduce the notion of scenario-based and policy-based
MAS collaboration, and analyze different MAS inter-agent structural and control architectures, including hierarchical and
federated architectures. This paper shows how scenario-based and policy-based collaboration matched with different
MAS control architectures fits the C2 requirements of urban combat operations.
We discuss the problem of making sense of very large amounts of multi-sensor data in terms of information fusion and
situation awareness. Our focus is on the application layer of the GIG in support of ISR analysis, sometimes referred to as
level 2 fusion or cognitive fusion. We discuss an approach where the key ontological constructs are events, event
correlation, situations, and situation assessment. We extend classic Belief-Desire-Intention (BDI) agents with situation
awareness, as a result of which the actions of BDI agents are triggered by situations rather than single events. We discuss
our reasoning mechanism against the background of ontology and knowledge base development and provide a simple
illustration of the concept towards opportunistic reasoning.
Cyber Security, Situation Management, and Impact Assessment II
8 April 2010 | Orlando, Florida, United States
Intelligent Sensing, Situation Management, and Impact Assessment
16 April 2009 | Orlando, Florida, United States
SC895: Introduction to Cognitive Situation Management for Tactical Operations
Modern wars are characterized by high mobility of troops and weapon systems, increasing operational tempo, and asymmetric and often unpredictable situations. Such new characteristics predicate the need for comprehensive and effective methods of battlespace situation management. Situation Management (SM) is as a synergistic goal-directed process of situation awareness, control, and prediction in dynamic operational spaces. The essential components of SM include sensing and intelligence gathering, information fusion and event correlation, modeling of the domain entities and their inter-relations, detecting and reasoning about the situations, threat situation prediction, and action planning affecting the situations. This course gives an overview of a new direction in situation management called cognitive (intelligent) situation management, i.e. on SM, which is associated with the meaning of situations and the logical methods of reasoning about the situations. In order to exhibit such intelligent capabilities, the systems should possess fairly elaborated conceptual knowledge about the domain (domain ontology).
The first section of the course describes the domain of cognitive situation management, reviews the issues, and gives introductory notions of modeling complex dynamic systems and operational situation management. The second section introduces the basic elements of the formal framework of cognitive situation management. The third section gives examples of situation management. The fourth section describes the core technologies of building situation management systems. The fifth section presents a distributed architecture of a situation management system based on a multi-agent approach, describes the software system architecture based on component services, and refers to several tools of building the situation management applications. The last section will discuss some advanced topics of situation management and outline future research and development directions. Overall, the course concentrates on practical aspects, requirements, basic concepts, architecture, design and key enabling technologies of building cognitive situation management systems.