Non-Markovian Agent Evolution with EVOLP

Logic Programming Update Languages were proposed as an extension of logic programming, which allow for modelling the dynamics of knowledge bases where both extensional knowledge (facts) as well as intentional knowledge (rules) may change over time due to updates, with important application Multi-Agent Systems (MAS). Despite their generality, these languages do not provide means to directly access past states of the evolving knowledge. They only allow for so-called Markovian changes i.e. changes determined entirely by the current state. This is a drawback in several situation. In this paper, after motivating the need for non-Markovian changes, we extend EVOLP -- The Logic Programming Update Language at the heart of an existing MAS -- with LTL-like temporal operators that allow referring to the history of the evolving agent. We then show that with a suitable introduction of new propositional variables it is possible to embed the extended EVOLP into the original one, thus demonstrating that EVOLP itself can already be used for non-Markovian changes. While showing how to use EVOLP for encoding non-Markovian changes, this embedding sheds light into the relationship between Logic Programming Update Languages and Modal Temporal Logics, of particular importance in MAS

Pure-Past Linear Temporal and Dynamic Logic on Finite Traces

LTLf and LDLf are well-known logics on finite traces. We review PLTLf and PLDLf, their pure- past versions. These are interpreted backward from the end of the trace towards the beginning. Because of this, we can exploit a foundational result on reverse languages to get an exponential improvement, wrt LTLf /LDLf, in computing the corresponding DFA. This exponential improvement is reflected in several forms sequential decision making involving temporal specifications, such as planning and decision problems in non-deterministic and non-Markovian domains. Interestingly, PLTLf (resp. PLDLf ) has the same expressive power as LTLf (resp. LDLf ), but transforming a PLTLf (resp. PLDLf ) formula into its equivalent in LTLf (resp. LDLf ) is quite expensive. Hence, to take advantage of the exponential improvement, properties of interest must be directly expressed in PLTLf /PLTLf

FOND planning for pure-past linear temporal logic goals

Recently, Pure-Past Temporal Logic (PPLTL) has proven highly effective in specifying temporally extended goals in deterministic planning domains. In this paper, we show its effectiveness also for fully observable nondeterministic (FOND) planning, both for strong and strong-cyclic plans. We present a notably simple encoding of FOND planning for PPLTL goals into standard FOND planning for final-state goals. The encoding only introduces few fluents (at most linear in the PPLTL goal) without adding any spurious action and allows planners to lazily build the relevant part of the deterministic automaton for the goal formula on-the-fly during the search. We formally prove its correctness, implement it in a tool called Plan4Past, and experimentally show its practical effectiveness

Universal Memory Architectures for Autonomous Machines

We propose a self-organizing memory architecture (UMA) for perceptual experience provably capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent’s environment. The architecture is simple enough to ensure (1) a quadratic bound (in the number of available sensors) on space requirements, and (2) a quadratic bound on the time-complexity of the update-execute cycle. At the same time, it is sufficiently complex to provide the agent with an internal representation which is (3) minimal among all representations which account for every sensory equivalence class consistent with the agent’s belief state; (4) capable, in principle, of recovering a topological model of the problem space; and (5) learnable with arbitrary precision through a random application of the available actions. These provable properties — both the trainability and the operational efficacy of an effectively trained memory structure — exploit a duality between weak poc sets — a symbolic (discrete) representation of subset nesting relations — and non-positively curved cubical complexes, whose rich convexity theory underlies the planning cycle of the proposed architecture

Non-Markovian Control in the Situation Calculus

The property that the executability and the effects of an action are determined entirely by the current state or situation is known as the Markov property and is assumed in most formalizations of action. The fact is, however, that it is not difficult to run into scenarios where the Markov property is not present. We consider removing this assumption from the situation calculus based formalization of actions of Reiter, which forms the basis of the programming language GOLOG, and define an operator for regressing formulas that quantify over past situations with respect to such nonMarkovian basic action theories.

Non-Markovian Control in the Situation Calculus

The property that the executability and the effects of an action are determined entirely by the current state or situation is known as the Markov property and is assumed in most formalizations of action. It is not difficult, however, to run into scenarios when the Markov property is not present. We consider removing this assumption from the situation calculus based formalization of actions of Reiter, which forms the basis of the programming language Golog, and define an operator for regressing formulas that quantify over past situations, with respect to such nonMarkovian basic action theories