Learning action-oriented models through active inference

Converging theories suggest that organisms learn and exploit probabilistic models of their environment. However, it remains unclear how such models can be learned in practice. The open-ended complexity of natural environments means that it is generally infeasible for organisms to model their environment comprehensively. Alternatively, action-oriented models attempt to encode a parsimonious representation of adaptive agent-environment interactions. One approach to learning action-oriented models is to learn online in the presence of goal-directed behaviours. This constrains an agent to behaviourally relevant trajectories, reducing the diversity of the data a model need account for. Unfortunately, this approach can cause models to prematurely converge to sub-optimal solutions, through a process we refer to as a bad-bootstrap. Here, we exploit the normative framework of active inference to show that efficient action-oriented models can be learned by balancing goal-oriented and epistemic (information-seeking) behaviours in a principled manner. We illustrate our approach using a simple agent-based model of bacterial chemotaxis. We first demonstrate that learning via goal-directed behaviour indeed constrains models to behaviorally relevant aspects of the environment, but that this approach is prone to sub-optimal convergence. We then demonstrate that epistemic behaviours facilitate the construction of accurate and comprehensive models, but that these models are not tailored to any specific behavioural niche and are therefore less efficient in their use of data. Finally, we show that active inference agents learn models that are parsimonious, tailored to action, and which avoid bad bootstraps and sub-optimal convergence. Critically, our results indicate that models learned through active inference can support adaptive behaviour in spite of, and indeed because of, their departure from veridical representations of the environment. Our approach provides a principled method for learning adaptive models from limited interactions with an environment, highlighting a route to sample efficient learning algorithms

Automated Verification of Quantum Protocols using MCMAS

We present a methodology for the automated verification of quantum protocols using MCMAS, a symbolic model checker for multi-agent systems The method is based on the logical framework developed by D'Hondt and Panangaden for investigating epistemic and temporal properties, built on the model for Distributed Measurement-based Quantum Computation (DMC), an extension of the Measurement Calculus to distributed quantum systems. We describe the translation map from DMC to interpreted systems, the typical formalism for reasoning about time and knowledge in multi-agent systems. Then, we introduce dmc2ispl, a compiler into the input language of the MCMAS model checker. We demonstrate the technique by verifying the Quantum Teleportation Protocol, and discuss the performance of the tool.Comment: In Proceedings QAPL 2012, arXiv:1207.055

Towards formal models and languages for verifiable Multi-Robot Systems

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

Interactive and common knowledge in the state-space model

This paper deals with the prevailing formal model for knowledge in contemporary economics, namely the state-space model introduced by Robert Aumann in 1976. In particular, the paper addresses the following question arising in this formalism: in order to state that an event is interactively or commonly known among a group of agents, do we need to assume that each of them knows how the information is imparted to the others? Aumann answered in the negative, but his arguments apply only to canonical, i.e., completely specified state spaces, while in most applications the state space is not canonical. This paper addresses the same question along original lines, demonstrating that the answer is negative for both canonical and not-canonical state spaces. Further, it shows that this result ensues from two counterintuitive properties held by knowledge in the state-space model, namely Substitutivity and Monotonicity.

Verifying Security Properties in Unbounded Multiagent Systems

We study the problem of analysing the security for an unbounded number of concurrent sessions of a cryptographic protocol. Our formal model accounts for an arbitrary number of agents involved in a protocol-exchange which is subverted by a Dolev-Yao attacker. We define the parameterised model checking problem with respect to security requirements expressed in temporal-epistemic logics. We formulate sufficient conditions for solving this problem, by analysing several finite models of the system. We primarily explore authentication and key-establishment as part of a larger class of protocols and security requirements amenable to our methodology. We introduce a tool implementing the technique, and we validate it by verifying the NSPK and ASRPC protocols

Assessing context-based learning: Not only rigorous but also relevant

Economic factors are driving significant change in higher education. There is increasing responsiveness to market demand for vocational courses and a growing appreciation of the importance of procedural (tacit) knowledge to service the needs of the Knowledge Economy; the skills in demand are information analysis, collaborative working and 'just-in-time learning'. New pedagogical methods go some way to accommodate these skills, situating learning in context and employing information and communications technology to present realistic simulations and facilitate collaborative exchange. However, what have so far proved resistant to change are the practices of assessment. This paper endorses the case for a scholarship of assessment and proposes the development of technology-supported tools and techniques to assess context-based learning. It also recommends a fundamental rethink of the norm-referenced and summative assessment of propositional knowledge as the principal criterion for student success in universities

Thinking Twice about Virtue and Vice: Philosophical Situationism and the Vicious Minds Hypothesis

This paper provides an empirical defense of credit theories of knowing against Mark Alfano’s challenges to them based on his theses of inferential cognitive situationism and of epistemic situationism. In order to support the claim that credit theories can treat many cases of cognitive success through heuristic cognitive strategies as credit-conferring, the paper develops the compatibility between virtue epistemologies qua credit theories, and dual-process theories in cognitive psychology. It also a response to Lauren Olin and John Doris’ “vicious minds” thesis, and their “tradeoff problem” for virtue theories. A genuine convergence between virtue epistemology and dual-process theory is called for, while acknowledging that this effort may demand new and more empirically well-informed projects on both sides of the division between Conservative virtue epistemology (including the credit theory of knowing) and Autonomous virtue epistemology (including projects for providing guidance to epistemic agents)