Multi-agent based supply chain management with dynamic reconfiguration capability

Supply chain management (SCM) has received increased attention in a globally challenging environment as companies face the necessity to improve customer service and maximize profit. Therefore, dynamic reconfiguration capability is vital for supply chain management to respond to changing customer requirements and operating environments. On the other hand, for its flexible and autonomous characteristics, multi-agent systems are a viable technology for SCM, and have been widely applied in SCM. To this end, dynamic reconfiguration in agent-based SCM systems is proposed from autonomy oriented computing point of view. The performance of agent-based SCM with dynamic reconfiguration is evaluated under a modified TAC SCM scenario. With a dynamic reconfigurable SCM system, new products and processes can be introduced with considerably less expense and ramp-up time.

Finite-time consensus algorithm of multi-agent networks

This paper is concerned with leader-follower finite-time consensus control of multi-agent networks with input disturbances. Terminal sliding mode control scheme is used to design the distributed control law. A new terminal sliding mode surface is proposed to guarantee finite-time consensus under fixed topology, with the common assumption that the position and the velocity of the active leader is known to its neighbors only. By using the finite-time Lyapunov stability theorem, it is shown that if the directed graph of the network has a directed spanning tree, then the terminal sliding mode control law can guarantee finite-time consensus even under the assumption that the time-varying control input of the active leader is unknown to any follower.

Incorporating fairness into development of an integrated multi-agent online dispute resolution environment

The paper describes the development of an integrated multi-agent online dispute resolution environment called IMODRE that was designed to assist parties involved in Australian family law disputes achieve legally fairer negotiated outcomes. The system extends our previous work in developing negotiation support systems Family_Winner and AssetDivider. In this environment one agent uses a Bayesian Belief Network expertly modeled with knowledge of the Australian Family Law domain to advise disputants of their Best Alternatives to Negotiated Agreements. Another agent incorporates the percentage split of marital property into an integrative bargaining process and applies heuristics and game theory to equitably distribute marital property assets and facilitate further trade-offs. We use this system to add greater fairness to Family property law negotiations.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

Predicting landscape-genetic consequences of habitat loss, fragmentation and mobility for multiple species of woodland birds

Inference concerning the impact of habitat fragmentation on dispersal and gene flow is a key theme in landscape genetics. Recently, the ability of established approaches to identify reliably the differential effects of landscape structure (e.g. land-cover composition, remnant vegetation configuration and extent) on the mobility of organisms has been questioned. More explicit methods of predicting and testing for such effects must move beyond post hoc explanations for single landscapes and species. Here, we document a process for making a priori predictions, using existing spatial and ecological data and expert opinion, of the effects of landscape structure on genetic structure of multiple species across replicated landscape blocks. We compare the results of two common methods for estimating the influence of landscape structure on effective distance: least-cost path analysis and isolation-by-resistance. We present a series of alternative models of genetic connectivity in the study area, represented by different landscape resistance surfaces for calculating effective distance, and identify appropriate null models. The process is applied to ten species of sympatric woodland-dependant birds. For each species, we rank a priori the expectation of fit of genetic response to the models according to the expected response of birds to loss of structural connectivity and landscape-scale tree-cover. These rankings (our hypotheses) are presented for testing with empirical genetic data in a subsequent contribution. We propose that this replicated landscape, multi-species approach offers a robust method for identifying the likely effects of landscape fragmentation on dispersal.

A general pose estimation algorithm in a multi-Kinect system

 Microsoft Kinect which has been primarily aimed at the computer gaming industry has been used in bio-kinematic research related implementations. A multi-Kinect system can be useful in exploiting spatial diversity to increase measurement accuracy. One of the main problems in deploying multi-Kinect systems is to estimate the pose, including the position and orientation of each Kinect. In this paper, a singular value decomposition (SVD) least-squares algorithm is extended to a more generic time-series based approach to solve this pose estimation problem utilising 3D positions of one or more joints in skeletons obtained from a multi-Kinect system. Additionally, computer simulations are performed to demonstrate the use and to evaluate the efficiency of the proposed algorithm. The former is further validated with a commercial Vicon system.

Selecting umbrella species for conservation: a test of habitat models and niche overlap for beach-nesting birds

Umbrella species are rarely selected systematically from a range of candidate species. On sandy beaches, birds that nest on the upper beach or in dunes are threatened globally and hence are prime candidates for conservation intervention and putative umbrella species status. Here we use a maximum-likelihood, multi-species distribution modeling approach to select an appropriate conservation umbrella from a group of candidate species occupying similar habitats. We identify overlap in spatial extent and niche characteristics among four beach-nesting bird species of conservation concern, American oystercatchers (Haematopus palliatus), black skimmers (Rynchops niger), least terns (Sterna antillarum) and piping plovers (Charadrius melodus), across their entire breeding range in New Jersey, USA. We quantify the benefit and efficiency of using each species as a candidate umbrella on the remaining group. Piping plover nesting habitat encompassed 86% of the least tern habitat but only 15% and 13% of the black skimmer and American oystercatcher habitat, respectively. However, plovers co-occur with all three species across 66% of their total nesting habitat extent (~ 649 ha), suggesting their value as an umbrella at the local scale. American oystercatcher nesting habitat covers 100%, 99% and 47% of piping plover, least tern and black skimmer habitat, making this species more appropriate conservation umbrellas at a regional scale. Our results demonstrate that the choice of umbrella species requires explicit consideration of spatial scale and an understanding of the habitat attributes that an umbrella species represents and to which extent it encompasses other species of conservation interest. Notwithstanding the attractiveness of the umbrella species concept, local conservation interventions especially for breeding individuals in small populations may still be needed.

Decision aggregation in an agent-based financial investment planning system

Agent technology provides a new way to model many complex problems like financial investment planning. With this observation in mind, a financial investment planning system was developed from agent perspectives with 12 different agents integrated. Some of the agents have similar problem solving and decision making capabilities. The results from these agents require to be combined. Ordered Weighted Averaging (OWA) operator was chosen to aggregate different results. Details on how OWA was applied as well as appropriate evaluation are presented.

An improvement to matchmaking algorithms for middle agents

A question frequently asked in multi-agent systems (MASs) concerns the efficient search for suitable agents to solve a specific problem. To answer this question, different types of middle agents are usually employed. The performance of middle agents relies heavily on the matchmaking algorithms used. Matchmaking is the process of finding an appropriate provider for a requester through a middle agent. There has been substantial work on matchmaking in different kinds of middle agents. To our knowledge, almost all currently used matchmaking algorithms missed one point when doing matchmaking -- the matchmaking is only based on the advertised capabilities of provider agents. The actual performance of provider agents in accomplishing delegated tasks is not considered at all. This results in the inaccuracy of the matchmaking outcomes as well as the random selection of provider agents with the same advertised capabilities. The quality of service of different service provider agents varies from one agent to another even though they claimed they have the same capabilities. To this end, it is argued that the practical performance of service provider agents has a significant impact on the matchmaking outcomes of middle agents. An improvement to matchmaking algorithms is proposed, which makes the algorithms have the ability to consider the track records of agents in accomplishing delegated tasks. How to represent, accumulate, and use track records as well as how to give initial values for track records in the algorithm are discussed. A prototype is also built to verify the algorithm. Based on the improved algorithm, the matchmaking outcomes are more accurate and reasonable.

An agent-based hybrid intelligent system for foreign exchange trading

The foreign exchange (FX) market has many features including (1) Each trader’s payoff depends not only on his own behavior, but also on other traders’ decisions; (2) The number of traders is too large to make them all know the other dealers’ methods of decision making; and (3) The FX market has many levels. The FX market is complex because of these features. A diversity of techniques are required to deal with such complex problems. That is hybrid solutions are crucial for the FX market. On the other hand, research into the FX market has revealed that it demonstrates some characteristics of multi-agent systems such as autonomy, interaction, and emergence. To this end, an agent-based hybrid intelligent system was developed for FX trading, which is based on our proposed agent-based hybrid framework. This paper is to discuss the analysis, design, and implementation such a system. Some experimental results and comparisons with related works are also provided. The interest of this paper does not reside in improving the predictive capabilities of different FX models, but rather in how to integrate different models into one system under the unifying agent framework. The success of this system indicates that agent perspectives are very appropriate to model complex problems such as the FX trading.

Highly programmable surface

A highly programmable electro-mechanical surface is developed using an effective array of individual pins arranged in a gridform. Each pin can be independently raised or lowered to create a wide range of contoured surfaces. It was found that as the number of elements increased. high levels of accuracy could still be achieved. however the required processing power increased logarithmically. This finding was attributed to the large amounts of data being passed. and subsequently led to a second focus; various methods of data management and flow control techniques within large-scale multi elemental systems. Results indicated a large potential for highly programmable surfaces within industry to provide a computer controlled surface for rapid prototyping. The research also revealed the potential for such a device to be used as a HID within Haptic applications.

Robust parallel filtering design of a swarm tracking system

Swarming networks of mobile autonomous agents require inter-agent position information in order perform various tasks. The primary control input for the majority of current control strategies is inter-agent distance information. In this paper we provide a robust parallel filter based tracking scheme that allows a mobile agent to track other multiple mobile agents. The distance, angle, and relative position is given in a direct target tracking output. This allows the mobile agent to decide which information is best suited for the particular objective