Chancellery Research - Research Publications
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ItemNo Preview AvailableDirective Explanations for Actionable Explainability in Machine Learning Applications(ASSOC COMPUTING MACHINERY, 2023-12)In this article, we show that explanations of decisions made by machine learning systems can be improved by not only explaining why a decision was made but also explaining how an individual could obtain their desired outcome. We formally define the concept of directive explanations (those that offer specific actions an individual could take to achieve their desired outcome), introduce two forms of directive explanations (directive-specific and directive-generic), and describe how these can be generated computationally. We investigate people’s preference for and perception toward directive explanations through two online studies, one quantitative and the other qualitative, each covering two domains (the credit scoring domain and the employee satisfaction domain). We find a significant preference for both forms of directive explanations compared to non-directive counterfactual explanations. However, we also find that preferences are affected by many aspects, including individual preferences and social factors. We conclude that deciding what type of explanation to provide requires information about the recipients and other contextual information. This reinforces the need for a human-centered and context-specific approach to explainable AI.
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ItemStrengthening Australia’s cybersecurity regulations and incentives: Response to the Department of Home Affairs Discussion Paper( 2021)The development of the regulatory and incentives framework is a key opportunity to align Australian enterprises’ cybersecurity practice with latest research, particularly on consumer protections, and emerging cyber threats and security challenges. The Australian Government has an essential role in establishing incentives to encourage best practice and consequences to combat poor practice. It will be increasingly important for government at all levels to act as a role model, by following best practice in the conduct of its public business.
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ItemNo Preview Available'Knowing Whether' in Proper Epistemic Knowledge Bases(AAAI Press, 2016)Proper epistemic knowledge bases (PEKBs) are syntactic knowledge bases that use multi-agent epistemic logic to represent nested multi-agent knowledge and belief. PEKBs have certain syntactic restrictions that lead to desirable computational properties; primarily, a PEKB is a conjunction of modal literals, and therefore contains no disjunction. Sound entailment can be checked in polynomial time, and is complete for a large set of arbitrary formulae in logics Kn and KDn. In this paper, we extend PEKBs to deal with a restricted form of disjunction: 'knowing whether.' An agent i knows whether Q iff agent i knows Q or knows not Q; that is, []Q or []not(Q). In our experience, the ability to represent that an agent knows whether something holds is useful in many multi-agent domains. We represent knowing whether with a modal operator, and present sound polynomial-time entailment algorithms on PEKBs with the knowing whether operator in Kn and KDn, but which are complete for a smaller class of queries than standard PEKBs.
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ItemNo Preview AvailablePlanning for a Single Agent in a Multi-Agent Environment Using FOND(AAAI Press, 2016)Single-agent planning in a multi-agent environment is challenging because the actions of other agents can affect our ability to achieve a goal. From a given agent's perspective, actions of others can be viewed as non-deterministic outcomes of that agent's actions. While simple conceptually, this interpretation of planning in a multi-agent environment as non-deterministic planning remains challenging, not only due to the non-determinism resulting from others' actions, but because it is not clear how to compactly model the possible actions of others in the environment. In this paper, we cast the problem of planning in a multiagent environment as one of Fully-Observable Non-Deterministic (FOND) planning. We extend a non-deterministic planner to plan in a multi-agent setting, allowing non-deterministic planning technology to solve a new class of planning problems. To improve the efficiency in domains too large for solving optimally, we propose a technique to use the goals and possible actions of other agents to focus the search on a set of plausible actions. We evaluate our approach on existing and new multiagent benchmarks, demonstrating that modelling the other agents' goals improves the quality of the resulting solutions.
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ItemSocial planning for social HRI(arxiv, 2016)
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ItemSocial Planning for Trusted Autonomy(Springer International Publishing, 2018)In this chapter, we describe social planning mechanisms for constructing and representing explainable plans in human-agent interactions, addressing one aspect of what it will take to meet the requirements of a trusted autonomous system. Social planning is automated planning in which the planning agent maintains and reasons with an explicit model of the other agents, human or artificial, with which it interacts, including the humans’ goals, intentions, and beliefs, as well as their potential behaviours. The chapter includes a brief overview of the challenge of planning in human-agent teams, and an introduction to a recent body of technical work in multi-agent epistemic planning. The benefits of planning in the presence of nested belief reasoning and first-person multi-agent planning are illustrated in two scenarios, hence indicating how social planning could be used for planning human-agent interaction explicitly as part of an agent’s deliberation.
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ItemEfficient multi-agent epistemic planning: Teaching planners about nested belief(ELSEVIER, 2022-01)Many AI applications involve the interaction of multiple autonomous agents, requiring those agents to reason about their own beliefs, as well as those of other agents. However, planning involving nested beliefs is known to be computationally challenging. In this work, we address the task of synthesizing plans that necessitate reasoning about the beliefs of other agents. We plan from the perspective of a single agent with the potential for goals and actions that involve nested beliefs, non-homogeneous agents, co-present observations, and the ability for one agent to reason as if it were another. We formally characterize our notion of planning with nested belief, and subsequently demonstrate how to automatically convert such problems into problems that appeal to classical planning technology for solving efficiently. Our approach represents an important step towards applying the well-established field of automated planning to the challenging task of planning involving nested beliefs of multiple agents.
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ItemNo Preview AvailableExplainable Reinforcement Learning through a Causal Lens(ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, 2020)Prominent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen by referring to counterfactuals — things that did not happen. In this paper, we use causal models to derive causal explanations of the behaviour of model-free reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal relationships between variables of interest. This model is then used to generate explanations of behaviour based on counterfactual analysis of the causal model. We computationally evaluate the model in 6 domains and measure performance and task prediction accuracy. We report on a study with 120 participants who observe agents playing a real-time strategy game (Starcraft II) and then receive explanations of the agents' behaviour. We investigate: 1) participants' understanding gained by explanations through task prediction; 2) explanation satisfaction and 3) trust. Our results show that causal model explanations perform better on these measures compared to two other baseline explanation models.
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ItemDesigning Multi-Agent System Organisations for Flexible Runtime Behaviour(MDPI, 2020-08)We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.
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