Joonoo Kim

Challenges for trust, fraud and deception research in multi-agent systems

Discussions at the 5th Workshop on Deception, Fraud and Trust in Agent Societies held at the 1st International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2002) centered around many important research issues 1 . This paper attempts to challenge researchers in the community toward future work concerning three issues inspired by the workshops roundtable discussion: (1) distinguishing elements of an agents behavior that influence its trustworthiness, (2) building reputation-based trust models without relying on interaction, and (3) benchmarking trust modeling algorithms. Arguments justifying the validity of each problem are presented, and benefits from their solutions are enumerated.

Belief Revision Process Based on Trust: Agents Evaluating Reputation of Information Sources

In this paper, we propose a multi-agent belief revision algorithm that utilizes knowledge about the reliability or trustworthiness (reputation) of information sources. Incorporating reliability information into belief revision mechanisms is essential for agents in real world multi-agent systems. This research assumes the global truth is not available to individual agents and agents only maintain a local subjective perspective, which often is different from the perspective of others. This assumption is true for many domains where the global truth is not available (or infeasible to acquire and maintain) and the cost of collecting and maintaining a centralized global perspective is prohibitive. As an agent builds its local perspective, the variance on the quality of the incoming information depends on the originating information sources. Modeling the quality of incoming information is useful regardless of the level and type of security in a given system. This paper introduces the definition of the trust as the agents confidence in the ability and intention of an information source to deliver correct information and reputation as the amount of trust an information source has created for itself through interactions with other agents. This economical (or monetary) perspective of reputation, viewing reputation as an asset, serves as social law that mandates staying trustworthy to other agents. Algorithms (direct and indirect) maintaining the model of the reputations of other information sources are also introduced.

Sensible agents: an implemented multi-agent system and testbed

Sensible Agents have been engineered to solve distributed problems in complex, uncertain, and dynamic domains. Each Sensible Agent is composed of four modules: the Action Planner, Perspective Modeler, Conflict Resolution Advisor, and Autonomy Reasoner. These modules give Sensible Agents the abilities to plan, model, resolve individual conflicts, and change agent system organization. Two component suites provide a variety of user- oriented features: the Sensible Agent Run- time Environment (SARTE) and the Sensible Agent Testbed. The SARTE provides facilities for instantiating Sensible Agents, deploying a Sensible Agent system, and monitoring run- time operations. The Sensible Agents Testbed facilitates automated generation of parameter combinations for controlled experiments, deterministic and non-deterministic simulation, and configuration of Sensible Agents and data acquisition. Experimentation is a crucial step in gaining insight into the behavior of agents, as well as evidence toward or against hypotheses. Using a real- world example, this paper explains and demonstrates: (1) the functional capabilities of Sensible Agents, (2) the Sensible Agent Run- Time Environments facilities for monitoring and control of Sensible Agent systems and (3) the experimental set- up, monitoring, and analysis capabilities of the Sensible Agent Testbed.

Soft security: isolating unreliable agents from society

This paper introduces a multi-agent belief revision algorithm that utilizes knowledge about reliability or trustworthiness of information sources to evaluate incoming information and the sources providing that information. It also allows an agent to learn the trustworthiness of other agents using (1) dissimilarity measures (measures that show how much incorrect information from a particular information source) calculated from the proposed belief revision processes (Direct Trust Revision) and/or (2) communicated trust information from other agents (Recommended Trust Revision). A set of experiments are performed to validate and measure the performance of the proposed Trust Revision approaches. The performance (frequency response and correctness) of the proposed algorithm is analyzed in terms of delay time (the time required for the step response of an agents belief state to reach 50 percent of the ground truth value), maximum overshoot (the largest deviation of the belief value over the ground truth value during the transient state), and steady-state error (deviation of the belief value after the transient state). The results show a design trade off in better responsiveness to system configuration or environmental changes versus resilience to noise. An agent designer may either (1) select one of the Trust Revision algorithms proposed or (2) use both of them to achieve better performance at the cost of system resource such as computation power and communication bandwidth.

Infrastructure for Design, Deployment and Experimentation of Distributed Agent-based Systems: The Requirements, The Technologies, and An Example

This paper discusses infrastructure for design, development, and experimentation of multi-agent systems. Multi-agent system design requires determining (1) how domain requirements drive the use of agents and AI techniques, (2) what competencies agents need in a MAS, and (3) which techniques implement those competencies. Deployment requirements include code reuse, parallel development through formal standardized object specifications, multi-language and multi-platform support, simulation and experimentation facilities, and user interfaces to view internal module, agent, and system operations. We discuss how standard infrastructure technologies such as OMG IDL, OMG CORBA, Java, and VRML support these services. Empirical evaluation of complex software systems requires iteration through combinations of experimental parameters and recording desired data. Infrastructure software can ease the setup, running, and analysis of large-scale computational experiments. The development of the Sensible Agent Testbed and architecture over the past six years provides a concrete example. The design rationale for the Sensible Agent architecture emphasizes domain-independent requirements and rapid deployment to new application domains. The Sensible Agent Testbed is a suite of tools providing or assisting in setting up, running, visually monitoring, and chronicling empirical testing and operation of complex, distributed multi-agent systems. A thorough look at the various Sensible Agents infrastructure pieces illustrates the engineering principles essential for multi-agent infrastructure, while documenting the software for users.

Constructing and dynamically maintaining perspective-based agent models in a multi-agent environment

This paper describes a model that explicitly represents the declarative and behavioral knowledge of a goal-driven agent. The developed declarative and the behavioral models allow an agent to reason about itself, other agents, and the environment. The declarative model specifies data and services (capabilities) assigned to an agent while the behavioral model specifies the execution model of an agent (defined as states, transitions between states, and events affecting the transitions). An Extended Statechart (ESC) is used as the execution model. To maintain these models, a self-contained computational module called the Perspective Modeler is proposed and incorporated into the Sensible Agent Architecture. With the Perspective Modeler, a Sensible Agent has the capability to model itself, other agents, and the environment to generate more desirable behaviors. The Perspective Modeler is implemented as a CORBA object, as demonstrated successfully at the AAAI‘’98 Intelligent Systems Demonstration session held in Madison, WI. Submitted to Agents’99

Toward flexible and fault tolerant intelligent manufacturing: Sensible agents in shop-floor control

The use of agent-based systems may offer significant advantages over centralized hierarchical manufacturing systems, including distribution of control/processing and data as well as adaptable automated or semiautomated problem solving. However, simply applying any agent-based paradigm to manufacturing domains may not be enough to address the various demands of these domains. Manufacturing domains require agent-based problem solving to be flexible and tolerant of faulty information (e.g., due to faulty sensors), equipments, and communication links. These issues are addressed by research on Sensible Agents, capable of 1) Dynamic Adaptive Autonomy and 2) explicit Perspective Modeling. Dynamic Adaptive Autonomy allows Sensible Agents to change problem-solving roles during system operation to address dynamic factory floor conditions, while Perspective Models internal to a respective Sensible Agent serve to maintain knowledge about the agent itself, other agents, and the environment. The application of the Sensible Agent paradigm to a shop-floor control is demonstrated.

Problem-solving frameworks for sensible agents in an electronic market

The need for responsive, flexible agents is pervasive in the electronic commerce environment due to its complex, dynamic nature. Two critical aspects of agent capabilities are the ability to (1) classify agent behaviors according to autonomy level, and (2) adapt problem-solving roles to various situations during system operation. Sensible Agents, capable of Dynamic Adaptive Autonomy, have been developed to address these issues. A Sensible Agent’s “autonomy level” constitutes a description of the agent’s problem-solving role with respect to a particular goal. Problem-solving roles are defined along a spectrum of autonomy ranging from command-driven, to consensus, to locally autonomous/master. Dynamic Adaptive Autonomy allows Sensible Agents to change autonomy levels during system operation to meet the needs of a particular problem-solving situation. This paper provides an overview of the Sensible Agent Testbed and provides examples showing how this testbed can be used to simulate agent-based problem solving in electronic-commerce environments.