This work investigates the problem of optimising a partial-order plan’s (POP) flexibility through the simultaneous transformation of its action ordering and variable binding constraints. While the former has been extensively studied through the notions of deordering and reordering, the latter has received much less attention. We show that a plan’s variable bindings are often related to resource usage and their reinstantiation can yield more flexible plans. To do so, we extend existing POP optimality criteria to support variable reinstantiation, and prove that checking if a plan can be optimised further is NP-complete. We also propose a MaxSAT-based technique for increasing plan flexibility and provide a thorough experimental evaluation that suggests that there are benefits in action reinstantiation.

Diffusion models are powerful tools for understanding the spread of diverse content such as information, opinions and ideas through social networks. Although these models have been successfully used to study the spreading dynamics such as viral marketing, there are many real scenarios (e.g. vaccination, evacuation) that require a more complex model. Hence, we propose a new hybrid framework that combines diffusion modelling with cognitive agent modelling. The hybrid, generic framework is grounded on BDI (Belief-Desire-Intention), an advanced, efficient cognitive agent framework. We demonstrate our framework to a wildfire evacuation case study consisting of 5,000 agents. We then compare and analyse the diffusion outcomes of our model against two baseline models, the standard Linear Threshold (LT) model and a slightly modified version of the LT model, across 17 different input configurations. The results show (statistically) significant differences with the baselines for the majority of the configurations, highlighting the need for cognitive agents in diffusion modelling. The framework presented here provides the basis for modelling complex reasoning to capture diffusion phenomena in complex and dynamic social systems.

This work addresses the problem of encoding cognitive agents that are capable of complex reasoning beyond simple rules, within agent-based models (ABM). This is particularly important for social simulation where agents represent people. We provide a general solution to this problem through infrastructure that allows the integration of state-of-the-art Belief-Desire-Intention (BDI) and ABM systems. In this paper, we demonstrate how this infrastructure is being used to help emergency services in Australia plan for community evacuations.

Hierarchically structured agent plans are important for efficient planning and acting, and they also serve (among other things) to produce “richer” classical plans, composed not just of a sequence of primitive actions, but also “abstract” ones representing the supplied hierarchies. A crucial step for this and other approaches is deriving precondition and effect “summaries” from a given plan hierarchy. This paper provides mechanisms to do this for more pragmatic and conventional hierarchies than in the past. To this end, we formally define the notion of a precondition and an effect for a hierarchical plan; we present data structures and algorithms for automatically deriving this information; and we analyse the properties of the presented algorithms. We conclude the paper by detailing how our algorithms may be used together with a classical planner in order to obtain abstract plans.

MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. More recently there has been work to extend MATSim to allow its use in applications requiring what has been referred to as “within day replanning”. In the work described here we have coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations. The approach used allows for all agents to be “intelligent” or for some to be “intelligent”/reactive, while others operate according to plans that are static within a single day. The former is appropriate for simulations such as a bushfire evacuation, where all agents will be reacting to the changing environment. The latter is suited to introducing agents such as taxis into a standard MATSim simulation, as they cannot realistically have a predetermined plan, but must constantly respond to the current situation. We have prototype applications for both bushfire evacuation and taxis. By extending the capabilities of MATSim to allow agents to respond intelligently to changes in the environment, we facilitate the use of MATSim in a wide range of simulation applications. The work also opens the way for MATSim to be used alongside other simulation components, in a simulation integrating multiple components.

The growing use of agent-based modelling and simulation for complex systems analysis has led to the availability of numerous published models. However, reuse of existing models in new simulations, for studying new problems, is largely not attempted. This is mainly because there is no systematic way of integrating agent-based models, that deals with the nuances of complex interactions and overlaps in concepts between components, in the shared environment. In this paper we present an open source framework, called OpenSim, that allows such integrated simulations to be built in a modular way, by linking together agent-based and other models. OpenSim is designed to be easy to use, and we give examples of the kinds of simulations we have built with this framework.

Software testing remains the most widely used approach to verification in industry today, consuming between 30-50 percent of the entire development cost. Test input selection for intelligent agents presents a problem due to the very fact that the agents are intended to operate robustly under conditions which developers did not consider and would therefore be unlikely to test. Using methods to automatically generate and execute tests is one way to provide coverage of many conditions without significantly increasing cost. However, one problem using automatic generation and execution of tests is the oracle problem: How can we automatically decide if observed program behavior is correct with respect to its specification? In this paper, we present a model-based oracle generation method for unit testing belief-desire-intention agents. We develop a fault model based on the features of the core units to capture the types of faults that may be encountered and define how to automatically generate a partial, passive oracle from the agent design models. We evaluate both the fault model and the oracle generation by testing 14 agent systems. Over 400 issues were raised, and these were analyzed to ascertain whether they represented genuine faults or were false positives. We found that over 70 percent of issues raised were indicative of problems in either the design or the code. Of the 19 checks performed by our oracle, faults were found by all but 5 of these checks. We also found that 8 out the 11 fault types identified in our fault model exhibited at least one fault. The evaluation indicates that the fault model is a productive conceptualization of the problems to be expected in agent unit testing and that the oracle is able to find a substantial number of such faults with relatively small overhead in terms of false positives.

One of the biggest bottlenecks for conversational systems is large-scale provision of suitable content. Our approach readily provides this without the need for custom-crafting. In this demonstration, we present the use of question-answer (QA) pairs mined from online question-and-answer websites to construct system utterances for a conversational agent. Our system uses QA pairs to formulate utterances that drive a conversation in addition to the answering of user questions as has been done in previous work. We use a collection of strategies that specify how and when the different parts of our question-answer pairs can be used and augmented with a small number of generic hand-crafted text snippets to generate natural and coherent system utterances.

Studies have shown that natural language interfaces such as question answering and conversational systems allow information to be accessed and understood more easily by users who are unfamiliar with the nuances of the delivery mechanisms (e.g., keyword‐based search engines) or have limited literacy in certain domains (e.g., unable to comprehend health‐related content due to terminology barrier). In particular, the increasing use of the web for health information prompts us to reexamine our existing delivery mechanisms. We present enquireMe, which is a contextual question answering system that provides lay users with the ability to obtain responses about a wide range of health topics by vaguely expressing at the start and gradually refining their information needs over the course of an interaction session using natural language. enquireMe allows the users to engage in “conversations” about their health concerns, a process that can be therapeutic in itself. The system uses community‐driven question–answer pairs from the web together with a decay model to deliver the top scoring answers as responses to the users' unrestricted inputs. We evaluated enquireMe using benchmark data from WebMD and TREC to assess the accuracy of system‐generated answers. Despite the absence of complex knowledge acquisition and deep language processing, enquireMe is comparable to the state‐of‐the‐art question answering systems such as START as well as those interactive systems from TREC.

More and more people are turning to the World Wide Web for learning and sharing information about their health using search engines, forums and question answering systems. In this demonstration, we look at a new way of delivering health information to the end-users via coherent conversations. The proposed conversational system allows the end-users to vaguely express and gradually refine their information needs using only natural language questions or statements as input. We provide example scenarios in this demonstration to illustrate the inadequacies of current delivery mechanisms and highlight the innovative aspects of the proposed conversational system.

There is great promise in the idea of having Web services available on the Internet, that can be flexibly composed to achieve more complex services, which can themselves then also be used as components in other contexts. However it is challenging to realise this idea, without essentially programming the composition using some process language such as WS-BPEL or OWL-S process descriptions. This paper presents a mechanism for specifying the external interface to composite and component services, and then deriving an appropriate internal model to realise a functioning composition. We present a conversation specification language for defining interaction protocols and investigate the issue of synchronous and asynchronous communication between the composite service and the component services

In organized health care, primary care is the first level. It is characterized by the wide span of health problems managed as well as remote location from traditional medical information and knowledge sources. The LIMEDS project has formulated the special requirements for integrated knowledge and data base management in primary care. This paper presents Gösta's book, a hypertext knowledge base implemented in LINCKS, an object oriented, networked database system. Firstly, aspects which make integrated hypermedia systems particularly suitable for application in primary health care are explored. We then describe the hypertext knowledge base, consisting of 500 basic text objects and 3000 links, and current implementations using the NODE data model. NODE is implemented on a SUN III fileserver, and the user interface for the hypertext context on Apple Macintosh (TM). Combination of design methods towards a parallel means-ends strategy was found to be necessary to achieve Gösta's book. Design groups need to be composed of computer science, medical, psychological and organizational competences.