Joseph A. Giampapa

Vulnerability Assessment of AC State Estimation With Respect to False Data Injection Cyber-Attacks

This paper introduces new analytical techniques for performing vulnerability analysis of state estimation when it is subject to a hidden false data injection cyber-attack on a power grids SCADA system. Specifically, we consider ac state estimation and describe how the physical properties of the system can be used as an advantage in protecting the power system from such an attack. We present an algorithm based on graph theory which allows determining how many and which measurement signals an attacker will attack in order to minimize his efforts in keeping the attack hidden from bad data detection. This provides guidance on which measurements are vulnerable and need increased protection. Hence, this paper provides insights into the vulnerabilities but also the inherent strengths provided by ac state estimation and network topology features such as buses without power injections.

Bilateral negotiation decisions with uncertain dynamic outside options

Negotiation is an important phase of e-contracting. E-contracting requires a proper negotiation model to effectively support negotiation decisions or automate the negotiation process. When an entity negotiates with a potential contractor, there may be some alternatives that exist simultaneously with the potential contractor, and/or some may present themselves in the future. We present a model for bilateral contract negotiations that considers the uncertain and dynamic outside options. Outside options affect the negotiation strategies via their impact on the reservation price. The model is composed of three modules, single-threaded negotiations, synchronized multi-threaded negotiations, and dynamic multi-threaded negotiations. These three models embody increased sophistication and complexity. The single-threaded negotiation model provides negotiation strategies without specifically considering outside options. The model of synchronized multi-threaded negotiations builds on the single-threaded negotiation model and considers the presence of concurrently existing outside options. The model of dynamic multi-threaded negotiations expands the synchronized multi-threaded model by considering the uncertain outside options that may come dynamically in the future. Experimental analysis is provided to characterize the impact of outside options on the negotiation strategy and performances.

The RETSINA MAS, a case study

In this paper we identify challenges that confront the large-scale multi-agent system (LMAS) designer, and claim that these challenges can be successfully addressed by agent-based software engineering (ABSE), which we consider to be distinct from object-oriented software engineering for multi-agent systems (OOSE for MAS) in its consideration of agent goal, role, context and attitude as first class objects. We show how we have discovered these principles through our experiences in developing the RETSINA multi-agent system, in implementing specific test applications, and in the derivation of three distinct architectures that help guide and describe the designs of our systems: the individual agent architecture, the functional architecture, and the infrastructure architecture.

Agent Support for Policy-Driven Collaborative Mission Planning

In this paper, we describe how agents can support collaborative planning within international coalitions, formed in an ad hoc fashion as a response to military and humanitarian crises. As these coalitions are formed rapidly and without much lead time or co-training, human planners may be required to observe a plethora of policies that direct their planning effort. In a series of experiments, we show how agents can support human planners, ease their cognitive burden by giving advice on the correct use of policies and catch possible violations. The experiments show that agents can effectively prevent policy violations with no significant extra cost.

An integrated approach to high-level information fusion

In todays fast paced military operational environment, vast amounts of information must be sorted out and fused not only to allow commanders to make situation assessments, but also to support the generation of hypotheses about enemy force disposition and enemy intent. Current information fusion technology has the following two limitations. First, current approaches do not consider the battlefield context as a first class entity. In contrast, we consider situational context in terms of terrain analysis and inference. Second, there are no integrated and implemented models of the high-level fusion process. This paper describes the HiLIFE (High-Level Information Fusion Environment) computational framework for seamless integration of high levels of fusion (levels 2, 3 and 4). The crucial components of HiLIFE that we present in this paper are: (1) multi-sensor fusion algorithms and their performance results that operate in heterogeneous sensor networks to determine not only single targets but also force aggregates, (2) computational approaches for terrain-based analysis and inference that automatically combine low-level terrain features (such as forested areas, rivers, etc.) and additional information, such as weather, and transforms them into high-level militarily relevant abstractions, such as NO-GO, SLOW-GO areas, avenues of approach, and engagement areas, (3) a model for inferring adversary intent by mapping sensor readings of opponent forces to possible opponent goals and actions, and (4) sensor management for positioning intelligence collection assets for further data acquisition. The HiLIFE framework closes the loop on information fusion by specifying how the different components can computationally work together in a coherent system. Furthermore, the framework is inspired by a military process, the Intelligence Preparation of the Battlefield, that grounds the framework in practice. HiLIFE is integrated with a distributed military simulation system, OTBSAF, and the RETSINA multi-agent infrastructure to provide agile and sophisticated reasoning. In addition, the paper presents validation results of the automated terrain analysis that were obtained through experiments using military intelligence Subject Matter Experts (SMEs).

Conversational Case-Based Planning for Agent Team Coordination

This paper describes a prototype in which a conversational case-based reasoner, NaCoDAE, was agentified and inserted in the RETSINA multi-agent system. Its task was to determine agent roles within a heterogeneous society of agents, where the agents may use capability-based or team-oriented agent coordination strategies. There were three reasons for assigning this task to NaCoDAE: (1) to relieve the agents of the overhead of determining, for themselves, if they should be involved in the task, or not; (2) to convert seemingly unrelated data into contextually relevant knowledge -- as a case-based reasoning system, NaCo-DAE is particularly suited for applying apparently incoherent data to a wide variety of domain-specific situations; and (3) as a conversational CBR system, to both unobtrusively listen to human statements and to proactively dialogue with other agents in a more goal-directed approach to gathering relevant information. The cases maintained by NaCoDAE have question and answer components, which were originally intended to maintain the textual representations of questions and answers for humans. By associating agent capability descriptions and queries with the case questions, NaCoDAE also assumed the team role of a capability-based coordinator. By encoding fragments of HTN plan objectives in its case actions, we were able to convert NaCoDAE into a conversational case-based planner that served compositionally-generated HTN plan objectives, already populated with situation-relevant knowledge, for use by the RETSINA team-oriented agents.

Techniques and directions for building very large agent teams

We have developed probabilistic algorithms that leverage the associates network for distributed plan instantiation, role allocation, information sharing and adjustable autonomy with a team. By developing such new algorithms, we have been able to build teams of hundreds of cooperating agents, and test specific behaviors among tens of thousands of agents. In this paper, we describe the algorithms that we have developed, the tests that we subjected them to, and sketch some of the key challenges that remain to be addressed.

Extending the ONESAF Testbed into a C4ISR Testbed

This article describes how the modeling and simulation environment of the OneSAF Testbed Baseline (OTB) v1.0 has been extended to enable the testing of heterogeneous algorithms that are being designed for real-world C4ISR applications. This has been accomplished by building an architecture that extends functional and logical components of the OTB system in the following ways: the use of the OTB Compact Terrain Database for terrain analysis and preliminary threat assessment, the addition of the RETSINA-OTB Bridge for the real-time query and control of OTB entities, and the addition of new DIS-based sensor entities for interoperation with Command and Control algorithms, to name a few. This article illustrates how to make a few small but general extensions to a modeling and simulation system to create a larger testbed system with minimum impact on the native system and with great potential for the range of applications that can exploit it.

Configuration management for multi-agent systems

As heterogeneous distributed systems, multi-agent systems present some challenging configuration management issues. There are the problems of knowing how to allocate agents to computers, launch them on remote hosts, and once the agents have been launched, how to monitor their runtime status so as to manage computing resources effectively. In this paper, we present the RETSINA Configuration Manager, \emph{RECoMa}. We describe its architecture, how it uses agent infrastructure such as service discovery, to assist the multi-agent system administrator in allocating, launching, and monitoring a heterogeneous distributed agent system in a distributed and networked computing environment.\footnote{The authors would like to acknowledge the significant contributions of Matthew W. Easterday in his earlier and exhaustive implementations of configuration management programs and CM design proposals. This research has been sponsored in part by DARPA Grant F-30602-98-2-0138 and the Office of Naval Research Grant N-00014-96-16-1-1222.}

A Markov Random Field Model of Context for High-Level Information Fusion

This paper presents a method for inferring threat in a military campaign through matching of battle field entities to a doctrinal template. In this work the set of random variables denoting the possible template matches for the scenario entities is a realization of a Markov random field. This approach does not separate low level fusion from high level fusion but optimizes both simultaneously. The result of the added high level context is a method that is robust to false positive and false negative, or missed, sensor readings. Furthermore, the high level context helps to direct the search for the best template match. Empirical results illustrate the efficacy of the method both at identifying threats in the face of false negatives, and at negating false positives, as well as illustrating the reduced computational effort resulting from the incorporation of additional high-level context