91 research outputs found
Normative practical reasoning:an argumentation-based approach
Autonomous agents operating in a dynamic environment must be able to reason and make decisions about actions in pursuit of their goals. In addition, in a normative environment an agent's actions are not only directed by the agent's goals, but also by the norms imposed on the agent. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent's practical attitude such as goals, plans and norms. In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.</p
Normative practical reasoning:an argumentation-based approach
Autonomous agents operating in a dynamic environment must be able to reason and make decisions about actions in pursuit of their goals. In addition, in a normative environment an agent's actions are not only directed by the agent's goals, but also by the norms imposed on the agent. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent's practical attitude such as goals, plans and norms. In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.</p
CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
Rule-based surrogate models are an effective and interpretable way to
approximate a Deep Neural Network's (DNN) decision boundaries, allowing humans
to easily understand deep learning models. Current state-of-the-art
decompositional methods, which are those that consider the DNN's latent space
to extract more exact rule sets, manage to derive rule sets at high accuracy.
However, they a) do not guarantee that the surrogate model has learned from the
same variables as the DNN (alignment), b) only allow to optimise for a single
objective, such as accuracy, which can result in excessively large rule sets
(complexity), and c) use decision tree algorithms as intermediate models, which
can result in different explanations for the same DNN (stability). This paper
introduces the CGX (Column Generation eXplainer) to address these limitations -
a decompositional method using dual linear programming to extract rules from
the hidden representations of the DNN. This approach allows to optimise for any
number of objectives and empowers users to tweak the explanation model to their
needs. We evaluate our results on a wide variety of tasks and show that CGX
meets all three criteria, by having exact reproducibility of the explanation
model that guarantees stability and reduces the rule set size by >80%
(complexity) at equivalent or improved accuracy and fidelity across tasks
(alignment).Comment: Accepted at ICLR 2023 Workshop on Trustworthy Machine Learning for
Healthcar
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library.
Multi-Agent Reinforcement Learning (MARL) en-compasses a powerful class of methodologies that have beenapplied in a wide range of fields. An effective way to furtherempower these methodologies is to develop approaches and toolsthat could expand their interpretability and explainability. Inthis work, we introduce MARLeME: a MARL model extractionlibrary, designed to improve explainability of MARL systemsby approximating them with symbolic models. Symbolic modelsoffer a high degree of interpretability, well-defined properties,and verifiable behaviour. Consequently, they can be used toinspect and better understand the underlying MARL systemsand corresponding MARL agents, as well as to replace all/someof the agents that are particularly safety and security critical.In this work, we demonstrate how MARLeME can be appliedto two well-known case studies (Cooperative Navigation andRoboCup Takeaway), using extracted models based on AbstractArgumentation
Human inference beyond syllogisms: an approach using external graphical representations.
Research in psychology about reasoning has often been restricted to relatively inexpressive statements involving quantifiers (e.g. syllogisms). This is limited to situations that typically do not arise in practical settings, like ontology engineering. In order to provide an analysis of inference, we focus on reasoning tasks presented in external graphic representations where statements correspond to those involving multiple quantifiers and unary and binary relations. Our experiment measured participants' performance when reasoning with two notations. The first notation used topological constraints to convey information via node-link diagrams (i.e. graphs). The second used topological and spatial constraints to convey information (Euler diagrams with additional graph-like syntax). We found that topo-spatial representations were more effective for inferences than topological representations alone. Reasoning with statements involving multiple quantifiers was harder than reasoning with single quantifiers in topological representations, but not in topo-spatial representations. These findings are compared to those in sentential reasoning tasks
Argumentation-based Reasoning about Plans, Maintenance Goals and Norms
Peer reviewedPostprin
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