Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural models are effective at guiding program synthesis searches.

Kanerva (2014) suggested that it would be possible to construct a complete Lisp out of a vector-symbolic architecture. We present the general form of a vector-symbolic representation of the five Lisp elementary functions, lambda expressions, and other auxiliary functions, found in the Lisp 1.5 specification (McCarthy, 1960), which is near minimal and sufficient for Turing-completeness. Our specific implementation uses holographic reduced representations (Plate, 1995), with a lookup table cleanup memory. Lisp, as all Turing-complete languages, is a Cartesian closed category (nLab authors, 2024), unusual in its proximity to the mathematical abstraction. We discuss the mathematics, the purpose, and the significance of demonstrating vector-symbolic architectures' Cartesian-closedness, as well as the importance of explicitly including cleanup memories in the specification of the architecture.

Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural models are effective at guiding program synthesis searches. However, a common drawback of those approaches is the inability to handle iterative loops, higher-order functions, or lambda functions, thus limiting prior neural searches from synthesizing longer and more general programs. We address this gap by designing a search algorithm called LambdaBeam that can construct arbitrary lambda functions that compose operations within a given DSL. We create semantic vector representations of the execution behavior of the lambda functions and train a neural policy network to choose which lambdas to construct during search, and pass them as arguments to higher-order functions to perform looping computations. Our experiments show that LambdaBeam outperforms neural, symbolic, and LLM-based techniques in an integer list manipulation domain.

Stochastic Programming is a framework for modelling and solving problems of decision making under uncertainty. Stochastic Dynamic Programming is a branch of Stochastic Programming that takes a "functional equation" approach to the discovery of optimal policies. By leveraging constructs - lambda expressions, functional interfaces, collections and aggregate operators - implemented in Java to operationalise the MapReduce framework, jsdp provides a general purpose library for modelling and solving Stochastic Dynamic Programs.

NumPy and Pandas are two significantly popular modules found in Python. Both modules are very popular to be main components of Machine Learning and Neural Networks studies. This article is taking these modules on board to summarize their features. Python is developed by Guido van Rossum and first released at the beginning of 90's as an open source programming language. With the increasing interest on Python, users contributed their work to the community.

Most of us have been introduced to Python as an object-oriented language; a language exclusively using classes to build our programs. While classes, and objects, are easy to start working with, there are other ways to write your Python code. Languages like Java can make it hard to move away from object-oriented thinking, but Python makes it easy. Given that Python facilitates different approaches to writing code, a logical follow-up question is: what is a different way to write code? While there are several answers to this question, the most common alternative style of writing code is called functional programming.

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.

There is a vast supply of prior art that study models for mental processes. Some studies in psychology and philosophy approach it from an inner perspective in terms of experiences and percepts. Others such as neurobiology or connectionist-machines approach it externally by viewing the mind as complex circuit of neurons where each neuron is a primitive binary circuit. In this paper, we also model the mind as a place where a circuit grows, starting as a collection of primitive components at birth and then builds up incrementally in a bottom up fashion. A new node is formed by a simple composition of prior nodes when we undergo a repeated experience that can be described by that composition. Unlike neural networks, however, these circuits take "concepts" or "percepts" as inputs and outputs. Thus the growing circuits can be likened to a growing collection of lambda expressions that are built on top of one another in an attempt to compress the sensory input as a heuristic to bound its Kolmogorov Complexity.

PROLOG is a simple and powerful progamming language based on first-order logic. This paper examines two possible extensions to the language which would generally be considered "higher-order".t The first extension introduces lambda expressions and predicate variables so that functions and relations can be treated as 'first class' data objects. We argue that this extension does not add anything to the real power of the language. The other extension concerns the introduction of set expressions to denote the set of all (provable) solutions to some goal. We argue that this extension does indeed fill a real gap in the language, but must be defined with care.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.