MULTI-AGENT SYSTEMS FOR DATA-RICH, INFORMATION-POOR ENVIRONMENTS

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MULTI-AGENT SYSTEMS FOR DATA-RICH, INFORMATION-POOR ENVIRONMENTS

ABSTRACT

The recent development of sensors integrated with memory, power supply and wireless networking capabilities marks a new era in sensor technology, with wide ranging implications for both military and civilian domains.   The capability for ubiquitous and distributed sensing has lead to the possibility of data-rich and information-poor environments, where the ability to collect data has overtaken the ability to understand its relevance and importance to the overall system goals. If the benefits of the sensor technology developments are to reach end users, we need to address two key questions.  First, what data should be gathered given resource constraints like limited sensor battery power?  Second, what information should be shared with humans, and between humans, given their cognitive constraints?  This thesis focuses on development of agent-based information management algorithms and architectures that can deal with the massive amounts of data generated, without overloading the human operators.  Intelligent agent technology with its emphasis on autonomy provides a valuable paradigm for this problem.

This thesis mainly focuses on designing and building a market-based resource allocation architecture for sensor management in distributed sensor networks.  A second domain, supply chain management, examines the question of what information should be shared, and involved development of a collaborative sense-making application.

A market-based agent design is proposed for the distributed sensor management problem, where the different system units are regarded as various market entities. This approach has the ability to create a comprehensive sensor management paradigm that can optimally distribute non-commensurate sensor network resources (e.g., sensor attention, battery power, and transmission capacity) among the distributed consumers, operating in a co-operative or semi-cooperative environment.

A team-based agent design is proposed for collaborative sense-making in a multiechelon supply chain. The various supply-chain entities, including the data generating entities (like RF sensors) are treated as team members with specific roles in a multi-agent team, based on the multi-agent team framework, Collaborative Agents for Team work (CAST). This approach holds the promise of addressing the information needs of the individual agents without the causing the problems of information overload, using the CAST based pro-active information and knowledge delivery policies.

Chapter 1

 Introduction

1.1  Problem Definition and Motivation

Recent years have seen great developments in the field of sensor technology and its applications, both in military and civilian domains [1, 2] . Sensors, integrated with memory, power supply and wireless networking capability made distributed and ubiquitous sensing a reality [3] .  However, the huge data collection capacity afforded by such improved sensor systems places great strains on human, computational and storage resources.  Lack of sophisticated high level algorithms to appropriately harness the benefits of these sensor developments has created data-rich, information-poor (DRIP) environments.  High-level DRIP activities, such as sense-making, decision-making and resource allocation, require gathering and coordinating information spread across sensors, information processes, software agents, and humans. Requiring these interacting entities to share all their local information is infeasible since this could lead to information overload  or a violation of privacy issues.  Thus for the benefits of recent sensor technology developments to reach end users, without overloading them, automated and distributed information management algorithms need to be developed that can provide decision-making entities with access to significant time-critical information, while filtering out irrelevant data.

We believe that multi-agent technology with its emphasis on autonomy, modularity, and distributed design provides a natural paradigm for this problem domain.  Agent coordination and cooperation frameworks include market-oriented programming, negotiation-based interactions, and team-based interactions (see [4] for an exhaustive survey).  To demonstrate and test the effectiveness of multi-agent-based design for automated sense-making of data, we have chosen two different domains which have great influence from sensor technology, sensor management (SM) and supply chain management (SCM).  The bulk of this thesis focuses on sensor management in distributed networks.  For this study, the sensor manager is mainly concerned with directing the datacollecting entities, the sensors, to satisfy the information requirements of the higher-level users in the best possible way. That is, we approach the information-processing in a topdown fashion.  First, the users submit requests for information and the SM is required to task the sensors to best satisfy the information requests.  However, in the second domain, supply chain management (which is described in more detail in the Appendix),  our information-processing approach to a distributed RFID supply chain management problem is bottom-up.  Data-generating entities like RFID sensors generate periodic readings of various system variables. The information-processing algorithm governs access to the data generated so that individual agents are not overwhelmed and at the same time, have timely information available to take appropriate actions.

Both these domains and approaches have the following common attributes, which makes them interesting cases for studying multi-agent based design for distributed information management systems:

  1. Relevance and importance of data to overall system goals: In both domains, the ability to collect data has overtaken the ability to understand its relevance and importance to the overall system goals. The key to the successful utilization of the new data collection technologies is the ability to generate useful information and knowledge, from the collected data.
  2. Information consumers with independent goals: Both the supply chain entities and the consumers of a sensor network can have independent goals and objectives and function in a semi-cooperative environment.
  3. Real-time constraints: These environments are also characterized by strict real time considerations where time pressure is a crucial consideration in the decision making process.
  4. Relevant information is distributed amongst various independent entities:

Straight-forward decision making in these domains can be cast as an optimization problem. However, the variables of the optimization routine are spread as private information among the various distributed domain entities.

Thus, collaborative sense-making, or the ability of distributed entities to make collective sense of the environment in which they operate, is not a straightforward task. Information management architectures and algorithms based on a multi-agent system approach dovetails nicely with many of the requirements for sense-making in distributed systems. This chapter briefly introduces the challenges of information processing in SM and SCM and outlines the contributions this thesis offers to tackle them.

1.1.1  Sensor Management in Distributed Environments.

Sensor management can be defined as “a process which seeks to manage or coordinate the use of sensing resources in a manner that improves the process of data fusion and ultimately that of perception, synergistically” [5] .   Multi-sensor systems rely on data fusion techniques to combine data from multiple sensors and related information to achieve more specific inferences than achievable by using a single, independent sensor. Sensor management system’s responsibilities include automation of sensor allocation and moding, pointing and emission control, prioritization and scheduling of service requests, coordinating fusion requests with data collected from different sensor and sensor modules, supporting reconfiguration and degradation due to loss of sensors or sensor modes and communication of desired actions to the individual sensors [6] .

A functional model of data fusion, Joint Directors of Laboratories (JDL) data fusion processing model [6] , has been proposed that illustrates the primary functions, relevant information and databases, and interconnectivity required to perform data fusion. The model comprises four levels, which form a hierarchy of processing (see Figure1-1). Level 1 processing, known as object refinement, fuses positional and identity data from multiple sensors to determine entity identities and to form tracks.

Level 2 processing, known as situation refinement, aims to infer the meanings or patterns in the order of battlefield, by fusing the spatial and temporal relationships between entities. Level 3 processing performs threat refinement to assess enemy threat, including estimation of their lethality, composition, evaluation of indication and warnings of impending events, targeting and weapons assessment calculations. Level 4 performs process refinement, which is an ongoing monitoring and assessment of the fusion process to refine the process itself and to regulate the acquisition of data to achieve optimal results (see Figure 1-2).  Level 4 processing should consider mission constraints and requirements so that SM actions do not impede mission objectives.  Level 4 processing includes sensor management functions which entail determination of sensor availability, sensor scheduling, task prioritization, sensor health monitoring, handling communication channels, etc.

 
JDL
 
Level 4 Process: Process Refinement
Mission
Management
Target
Prediction
Sensor
&
Platform
Modeling
System
Performance
Modeling
System
Control
•Representation of mission objectives

•Mission constraints •Adjudication between fusion optimization &

•Target state estimation

•Target

attribute modeling

•Sensor platform models •Sensor characteristics •Signal propagation models

•Target/Sensor signal interaction

•Sensor performance models •Algorithm performance models •Measures of performance •Optimization criteria •Optimization algorithm(s) •Control philosophy

Figure 1-2:  JDL Level 4 process refinement

 

Sensor management algorithms map into Level 4 data fusion whose concern is the optimization of sensor or information sources utilization and algorithms to achieve the most useful set of information.  Most research efforts in the area of data fusion have concentrated on the lower levels of data fusion hierarchy such as development of algorithms of tracking, situation assessment and threat refinement. Process refinement and optimization in heterogeneous multi sensors has been an under researched area and therefore lacks coherent architectures and algorithms [7] . However, if the benefits of the recent developments in sensor technology are to reach the end users, development of data fusion level four algorithms is critical.  The recent developments in sensor technology have not had the complement of corresponding developments in sensor management algorithms, leading to data rich and information poor environments [9] . Modern sensors are integrated with computational power, energy, communication and memory resources. Simultaneous consideration of these non-commensurate measurements is a requirement for efficient use of a distributed heterogeneous sensor network, thus making sensor management a more ardent task than a simple, single-value optimization problem.   This research project developed a comprehensive sensor management algorithm that accounts for the heterogeneity of the sensors, threat levels in the environment, and provides for distributed and decentralized control.

1.1.2  Supply Chain Management

Ubiquitous sensing capabilities have great implications for supply chain management.  Real-time information from sensors can lead to more efficient manufacturing, distribution and logistics. Many companies including Wal-mart have invested heavily in Radio Frequency Identification (RFID) technology to revolutionize their supply chains [10] .  The basic idea behind RFID technology is to create smart shelves that monitor inventory levels.  Low-inventory generates an automatic signal to the store manager. This information propagates throughout the supply chain entities, including the distribution center and manufacturers. However, this supply chain mechanism involves significant data generation that can cause a problem of information overload to the supply chain manager. Thus, adequate, automated response systems throughout the supply chain are critical to reduce the data processing requirements of managers.  This study’s approach to this problem is to model the supply chain as a multiagent system, where each supply chain unit, including the data generating sensors is treated as agents. Our multi-agent design aims to create an information sharing environment where only the essential information required for coordination is communicated, so that individual agents are not overwhelmed with data. This requirement entails that agents anticipate each other’s information needs.  For this purpose, we use the concept of team-based agents where agents have a shared mental model. Team-based agents are aware of each other’s roles in the team and can thus reason about other’s information requirements.  This design prevents problems in supply chain that might occur due to a lack of timely intervention while simultaneously avoiding the problem of information overload at the same time.

1.2  Problem Scope

1.2.1 Sensor Management

The sensor manager has to account for a number of factors in the optimization of sensor utilization. The various parameters include threat levels in the environment, bandwidth and power requirements of the sensors, and expected performance level of a sensor for a particular task.  The sensor manager might also have to deal with requests for sensor resources from multiple information-seeking consumers. Furthermore, some of the sensors and consumers might be humans and so the sensor manager has to account for any “human in the loop” problem. The difficulty in converting all the concerned factors into commensurate measures that can be used in an optimization code is one of the factors that makes this problem a difficult one. Another factor is real-time constraints of the problem domain. Additionally, in current sensor-rich environments, where the consumers and sensor resources have spatio-temporal distribution, centralized control and optimization of the problem might not be feasible. In this situation, the sensor management algorithm requires distributed and decentralized control and must provide simultaneous consideration of diverse, incommensurate measures like bandwidth, sensor battery power, network processing power, etc. Previous sensor management techniques have relied on converting the diverse incommensurate measures into ad-hoc heuristic measures for use in some optimization algorithm.  These solutions to sensor management lack generalizability and suffer from being “point solutions” that are very specific to the domain in which they have been developed [11] .

An ideal solution to the sensor management problem includes the development of a system architecture and control algorithms that:

  1. is generalizable and can be adapted to wide range of sensor network domains
  2. provides for distributed, decentralized control
  3. provides “human in the loop” capability
  4. results in optimal (or sufficiently optimal) allocation of sensor resources.

This research project developed a comprehensive sensor management algorithm that posseses the above attributes, and thus can successfully account for the heterogeneity of the sensors, threat levels in the environment and provide for distributed and decentralized control.

1.2.2  Supply Chain Management

Collaborative sense-making, or the ability of business partners to jointly make sense of the environment, is becoming an increasingly important capability that senior executives should pursue in order to effectively manage their supply chains. The integration of the new sensing technologies with supply chains has created the possibility of generation massive amounts of potentially useful information [11] . Traditional decision support systems do not have the ability to deal with such magnitudes of data. Moreover, overwhelming executives with too much information is dangerous.  A recent study by Sutcliffe and Weber [12] concludes that for top-level executives, collecting information is less important than the interpretation of information.  Another danger for executives is over reliance on intuition, especially in new or dynamic situations, which can be biased and limited by human cognitive capabilities.

Although prior work on multi-agent architectures for supply chains exists [13-16] , they have not adequately leveraged the findings of the supply chain research community in their design. We believe that a comprehensive multi-agent design should have a firm grounding in the relevant research findings of management science. For this purpose, we have used the work of Hult et al. [17] to guide our design process.  Based on an extensive survey of supply chain and related literature, Hult, et al. [17] formulated a model to explain supply chain efficiency as revealed by its cycle-time in terms of its achieved memory, knowledge acquisition activities, information distribution activities and shared meaning. Achieved memory is defined as “the amount of knowledge, experience or familiarity with the supply chain process.” Shared meaning is “the extent to which participants develop common understanding about data and events.”

The following guidelines have been identified from the work of Hult, et al. as being relevant to the proposed agent architecture design:

  • Knowledge acquisition:Each member in a supply chain should have a systematic knowledge acquisition approach, guided by its achieved memory. Extending Grant [18] ’s view of firm to supply chains, supply chains are regarded a knowledge integrating entities, whose primary role is application of the knowledge acquired.  Knowledge acquisition or memory creation has been found to a prerequisite for development of shared meaning across supply chain which in turn decreases cycle time.
  • Shared Meaning: Creation of shared meaning enables members in a supply chain to reason about and interpret other’s actions and intentions. Shared meaning is a critical mechanism for communication and co-ordination within a supply chain [19] since the participating units lack a common culture [20] .
  • Information distribution:

Although supply chain effectiveness depends on the exchange of timely and accurate information across customers, material and service suppliers, and internal functional areas [21] , the tremendous rate at which modern information systems generate data can overwhelm supply chain units [20] . In a study of organizational learning, Huber [22] stated that the excessive information that exceeds a unit’s information processing capacity can adversely effect information interpretation within the unit. In information rich environments, “informational autonomy” between the various units might improve overall efficiency by addressing the problem of information overload. Thus, information flow can be postulated to have a curvilinear relation with outcomes, with an inflection point after which dealing with more information becomes overwhelming [17] .  Therefore, “information should be (is) distributed to only those who need it”. An intelligent information distribution mechanism allows the members to gather enough information about their environment while not overloading their cognitive capacities with excessive or redundant information.

This theoretical background offers the following directions for the supply chain multi-agent design research:

  • Agents should have a systematic knowledge acquisition procedure, guided by its previous experience and knowledge
  • Agents should have a mechanism for creation of shared meanings, since shared meaning provides the basis for supply chain co-ordination
  • Communication mechanisms should be provided so that timely

information is provided to the agents that require it while also taking care that redundant or excessive communication is avoided.

Based on the above principles, this research has developed a team-based multiagent framework for SCM that can potentially provide a comprehensive information processing approach for multi-tier supply chains.

MULTI-AGENT SYSTEMS FOR DATA-RICH, INFORMATION-POOR ENVIRONMENTS

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