In this paper, I present my ongoing research on temporal defeasible argumentation-based multi-agent planning. In multi-agent planning a team of agents share a set of goals but have diverse abilities and temporal beliefs, which vary over time. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of temporal plan proposals, plus temporal arguments against them, where both, actions with different duration, and temporal defeasible arguments, need to be integrated. This thesis proposes a computational framework for this research on multi-agent planning.

In this paper we describe a software assistant agent that can proactively assist human users situated in a time-constrained environment to perform normative reasoning--reasoning about prohibitions and obligations--so that the user can focus on her planning objectives. In order to provide proactive assistance, the agent must be able to 1) recognize the user's planned activities, 2) reason about potential needs of assistance associated with those predicted activities, and 3) plan to provide appropriate assistance suitable for newly identified user needs. To address these specific requirements, we develop an agent architecture that integrates user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. This paper presents the agent architecture and discusses practical applications of this approach.

This paper presents the current state of this research work, aimed to develop a methodology for designing Adaptive Virtual Organizations. This paper includes both completed and remaining work on this topic.

We report the results obtained during the verification of Autosub6000, an autonomous underwater vehicle used for deep oceanic exploration. Our starting point is the Simulink/Matlab engineering model of the submarine, which is discretised by a compiler into a representation suitable for model checking. We assess the ability of the vehicle to function under degraded conditions by injecting faults automatically into the discretised model. The resulting system is analysed by means of the model checker MCMAS, and conclusions are drawn on the system's ability to withstand faults and to perform self-diagnosis and recovery. We present lessons learnt from this and suggest a general method for verifying autonomous vehicles.

In heterogeneous multi-agent systems trust is necessary to improve interactions by enabling agents to choose good partners. Most trust models work by taking, in addition to direct experiences, other agents' communicated evaluations into account. However, in an open MAS other agents may use different trust models and the evaluations they communicate are based on different principles: as such they are meaningless without some form of alignment. My doctoral research gives a formal definition of this problem and proposes two methods of achieving an alignment.

HAPTIC is grounded in game theory and probabilistic modeling. It has been proved that My thesis contributes to the field of multi-agent HAPTIC agents learn other agents' behaviors reliably using systems by proposing a novel trust-based decision direct observations. One shortcoming of HAPTIC is that it model for supply chain management.

This requires, for Goal-driven autonomy (GDA) is a reflective model example, experts to anticipate what discrepancies can occur, of goal reasoning that controls the focus of an identify what goals can be formulated, and define their agent's planning activities by dynamically relative priority. However, few techniques have been resolving unexpected discrepancies in the world investigated for learning this knowledge, and those that do state, which frequently arise when solving tasks in learn only goal formulation knowledge (Weber et al. 2010; complex environments. GDA agents have Powell et al. 2011). This can be problematic; while these performed well on such tasks by integrating agents may perform well in simple environments, in others a methods for discrepancy recognition, explanation, domain expert might not know the (state) expectations for goal formulation, and goal management. However, executing every action in every state, nor which goal should they require substantial domain knowledge, be pursued to resolve every possible discrepancy, or even including what constitutes a discrepancy and how the space of all possible discrepancies.

Car pollution is one of the major causes of green-house emissions, and traffic congestion is rapidly becoming a social plague. Dynamic Ride Sharing (DRS) systems have the potential to mitigate this problem by computing plans for car drivers, e.g. commuters, allowing them to share their rides. Existing efforts in DRS are suffering from the problem that participants are abandoning the system after repeatedly failing to get a shared ride. In this paper we present an incentive compatible DRS solution based on auctions. While existing DRS systems are mainly focusing on fixed assignments that min- imize the totally travelled distance, the presented approach is adaptive to individual preferences of the participants. Furthermore, our system allows to tradeoff the minimization of Vehicle Kilometers Travelled (VKT) with the overall probability of successful ride-shares, which is an important fea- ture when bootstrapping the system. To the best of our knowledge, we are the first to present a DRS solution based on auctions using a sealed-bid second price scheme.

In agent-oriented programming and planning, agents' actions are typically specified in terms of postconditions, and the model of execution assumes that the environment carries the actions out exactly as specified. That is, it is assumed that the state of the environment after an action has been executed will satisfy its postcondition. In reality, however, such environments are rare: the actual execution of an action may fail, and the envisaged outcome is not met. We provide a conceptual framework for reasoning about success and failure of agents' behaviours. In particular, we propose a measure that reflects how "good" an environment is with respect to agent's capabilities and a given goal it might pursue. We also discuss which types of goals are worth pursuing, depending on the type of environment the agent is acting in.

We report on results from experiments where human traders interact with software-agent traders in a real-time asynchronous continuous double auction (CDA) experimental economics system. Our experiments are inspired by the seminal work reported by IBM at IJCAI 2001, where it was demonstrated that software-agent traders could consistently outperform human traders in real-time CDA markets. IBM tested two trading-agent strategies, ZIP and a modified version of GD, and in a subsequent paper they reported on a new strategy called GDX that was demonstrated to outperform GD and ZIP in agent vs. agent CDA competitions, on which basis it was claimed that GDX "...may offer the best performance of any published CDA bidding strategy.". In this paper, we employ experiment methods similar to those pioneered by IBM to test the performance of "Adaptive Aggressive" (AA) algorithmic traders. The results presented here confirm Vytelingum's claim that AA outperforms ZIP, GD, and GDX in agent vs. agent experiments. We then present the first results from testing AA against human traders in human vs. agent CDA experiments, and demonstrate that AA's performance against human traders is superior to that of ZIP, GD, and GDX. We therefore claim that, on the basis of the available evidence, AA may offer the best performance of any published bidding strategy.