REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Cognitive biases, heuristics and decision-making in design for behaviour change

Copyright @ 2012 Social Science Electronic PublishingMuch human behaviour can be seen as decision-making, and so understanding and influencing those decision-making processes could be an important component in design for behaviour change. This paper examines the 'heuristics and biases' approach to modelling decision-making, and attempts to extract insights which are relevant to designers working to influence user behaviour for social or environmental benefit -- either by exploiting biases, or helping to counter those which lead to undesirable behaviour. Areas covered include a number of specific cognitive biases in detail, and the alternative perspective of Gigerenzer and others, who contend (following Herbert Simon) that many heuristics potentially leading to biases are actually ecologically rational, and part of humans' adaptive responses to situations. The design relevance of this is briefly considered, and implications for designers are summarised

Regulation for Conservatives: Behavioral Economics and the Case for "Asymmetric Paternalism"

Regulation by the state can take a variety of forms. Some regulations are aimed entirely at redistribution, such as when we tax the rich and give to the poor. Other regulations seek to counteract externalities by restricting behavior in a way that imposes harm on an individual basis but yields net societal benefits. A good example is taxation to fund public goods such as roads. In such situations, an individual would be better off if she alone were exempt from the tax; she benefits when everyone (including herself) must pay the tax

Thoughts on financial derivatives, systematic risk, and central banking: a review of some recent developments

This paper critically reviews the literature examining the role of central banks in addressing systemic risk. We focus on how the growth in derivatives markets might affect that role. Analysis of systemic risk policy is hampered by the lack of a consensus theory of systemic risk. We propose a set of criteria that theories of systemic risk should satisfy, and we critically discuss a number of theories proposed in the literature. We argue that concerns about systemic effects of derivatives appear somewhat overstated. In particular, derivative markets do not appear unduly prone to systemic disturbances. Furthermore, derivative trading may increase informational efficiency of financial markets and provide instruments for more effective risk management. Both of these effects tend to reduce the danger of systemic crises. However, the complexity of derivative contracts (in particular, their high implicit leverage and nonlinear payoffs) do complicate the process of regulatory oversight. In addition, derivatives may make the conduct of monetary policy more difficult. Most theories of systemic risk imply a critical role for central banks as the ultimate provider of liquidity. However, the countervailing danger of moral hazard must be recognized and addressed through vigilant supervision.Banks and banking, Central ; Derivative securities ; Risk